Ch10. Word and text relatedness

Learning Objectives

Understand the goals and applications of the taks of word relatedness
Learn about corpus-based measures of word relatedness

Word Relatedness

Semantic relatedness involves identification and quantification of the strength of relationships in meaning that exist between textual units such as word pairs. When we make judgments about semantic relatedness or similarity in everyday life, we typically rely on our accumulated knowledge and experiences and utilize our capacities for conceptual thinking, abstraction, and generalization. For example, we know that the word democracy is closely related with the word election not only by learning knowledge from a book or a lecture but also by our previous experience with politics. Text mining gives us another opportunity to identify and measure the relationship between words. Modeling a semantic relatedness is one of the main tasks of the field of natural language processing. And this technique allows us to reveal word-based relatedness in a given corpus. In doing so, we can make a machine understand human language like the close relationship between “democracy” and “election”, which would not be known otherwise.

Theoretical Foundations

Basically, word-to-word relatedness is measured by distance-based metrics. For example, we can determine how semantically related as democracy and election by how closely the two words are located in semantic space. This semantic space is constructed by the patterns that each word co-occurs with other words in a pre-defined context like a sentence. The assumption underlying this method is that when the words a given pair of words co-occurs with frequently are similar to each other, then we determine the pair of words are semantically similar to one another. For example, we learn that the word democracy is semantically similar to the word election because the two words are frequently used together with politics, citizen, liberal, conservative, public, and so on in a corpus of news articles we can collect from the press. Because we measure sematic relatedness between democracy and election by their co-occurrence patterns in the corpus, this is called a corpus-based measure of word relatedness.

One thing to note is that semantic relatedness is different from semantic similarity. Similarity is a more specific concept than relatedness: Similarity is concerned with entities related by virtue of their likeness and that stay within a part of speech boundary; for example, ramen and noodle. So, when a word A is semantically similar to a word B, then B can substitute for A. But when it comes to relatedness, the words that are semantically dissimilar can be related like hot - cold or democracy - election.

From now on, I’d like to introduce you to three popular corpus-based measures of word relatedness.

Corpus-based Measures of Relatedness

Corpus-based measures of word semantic similarity or relatedness try to identify the degree of similarity between words using information exclusively derived from large corpora. There are three corpus-based measures that have been used more frequently: 1) pointwise mutual information (PMI; Turney, 2001), 2) latent semantic analysis (LSA; Landauer, Foltz, & Laham, 1998), and 3) word embeddings (Word2Vec; Mikolov, Yih, & Zweig, 2013).

Pointwise Mutual Information

Suggested by Turney (2001), the PMI is an unsupervised measure for the evaluation of the semantic similarity of words. It is based on word co-occurrence using counts collected over very large corpora. The logic behind this metric is that when a word A and a word B is semantically related, the words are more likely to co-occur with one another in a pre-determined window (usually 10 words or a sentence) than they occcur separately (or independently). For example, using the PMI, we know the words Chung-Ang and University are semantically connected to one another in a corpus of texts exclusive derived from the Chung-Ang In community on the web, because the two words are much more likely to be used togehter rather than they are used separately (independently).

The equation demonstrated on the page 108 in our main textbook measures the degree to which two words w1 and w2 occur together devided by the degrees to which each word occurs independently.

Latent Semantic Analysis

Another corpus-based measure of semantic similarity is LSA, proposed by Landauer and colleagues (1998). In LSA, term (word) co-occurrences in a corpus are captured by means of a dimensionality reduction operated by a singular value decomposition (SVD) on the term-by-document matrix T representing the corpus.

SVD is a well-known operation in linear algebra, which can be applied to any rectanglar matrix in order to find correlations among its rows and columns. But without any linear algebra background, it is not easy to understand the logic of SVD. Let me try to explain it in a simpler way. I think SVD can be explained by appealing to an intuitive application of matrix factorization. Matrix factorization can help explain lots of observations for very many particular things in terms of very few general things, and, that matches how many things in the world work, which is useful.

Let say I watch your bookshelf, then I see 100 different novels, from a world of a million books. Maybe I see Conan Doyle’s Sherlock Homes and Agatha Christie’s Murder on the Orient Express, which are very famous mystery novels. But I don’t believe you’d describe your fiction preferences this way, by listing 100 novels in your bookshelf. You’d like say “I like a mystery genre.” That’s not only more efficient to say, but reveals more – you likely have some affinity for hundreds other mystery novels. If we didn’t actually think and ‘like’ things in terms of genres, it’d be a lot harder to reason about tastes. Every book would be an island unto itself and say little about your preference for others. But, because we have the underlying idea of “Mystery”, suddenly by knowing these are “Mystery” novels I have a world of more informed guesses about your interest in other mystery novels like by Edgar Allan Poe.

SVD is trying to find those underlying features, “genres” in the case of novels. It will find the samll set of features that best explains some input – like a list of all novels owned by a bunch of readers. From these reader-novel associations we get two outputs, reader-feature associations (i.e. how much I like Mystery, History, Romantic, Fantasy, SF, etc.) and item-feature associations (i.e. how much each novel is a Mystery genre, a History genre, a Romantic genre, and so on). We also get a third output saying how relatively important these associations are in explaining novel tastes.

From these we can do useful things, not least of which are things like recommendation, filling in your bookshelf, with novels that this model predicts we like. We can efficiently compare novels’ / readers’ similarity in terms of few features.

Word Embeddings

The technique of word embeddings is one of the most recent methods to measure word relatedness. It is baesd on deep learning vectors learend using a skp-gram recurrent neural net architecture running overa large raw corpus (Mikolov, Yih, & Zweig, 2013). A primary advantage of word embeddings is that, by breaking away from the typical n-gram model that sees individual units with no relationship to one another, they are to generalize and produce word vectors that are similar for related words, thus encoding linguistic regularities and patterns. For example, assuming that the function vector() returns the word embedding for an input word, it has been shown that vector("Madrid")-vector("Spain")+vector("France") is closer to vector("Paris") than any other word vector. Google has made available a pretrained word embedding model called word2vec, which is built over a 100 billion-word corpus, and contains 3 million 300-dimension vectors for words and phrases.

R provides some packages that allow us to implement such word relatedness measures like PMI, LSA, and Word Embeddings. It also provides some datasets that contain millions word vectors from a pretrained word-embedding model like Word2Vec. When we are capable of using such packages, we are equipped with a new fancy skill to reveal not only word-to-word relatedness but also text-to-text relatedness.

R Practice for Tweet Sentiment Classification

Lexicon-based Sentiment Analysis III

Let’s preprocess tweet text

We know how to convert our tweet data into a tidy text data format.

library(tidyverse)

## -- Attaching packages -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- tidyverse 1.3.0 --

## √ ggplot2 3.3.0     √ purrr   0.3.4
## √ tibble  3.0.0     √ dplyr   0.8.5
## √ tidyr   1.0.2     √ stringr 1.4.0
## √ readr   1.3.1     √ forcats 0.5.0

## -- Conflicts ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()

library(tidytext)
library(lubridate)

## 
## Attaching package: 'lubridate'

## The following objects are masked from 'package:dplyr':
## 
##     intersect, setdiff, union

## The following objects are masked from 'package:base':
## 
##     date, intersect, setdiff, union

library(stringr)
library(stopwords)

load("covid_tweets_423.RData")
covid_tweets # This dataset contains 18,224 tweets about COVID-19 including geo-location information.

## # A tibble: 18,224 x 9
##    user_id status_id created_at          screen_name text  lang  country    lat
##    <chr>   <chr>     <dttm>              <chr>       <chr> <chr> <chr>    <dbl>
##  1 479491~ 12533658~ 2020-04-23 16:51:11 Vegastechh~ "@ev~ en    United~  36.0 
##  2 169480~ 12533658~ 2020-04-23 16:51:11 Coachjmorr~ "Ple~ en    United~  36.9 
##  3 215583~ 12533658~ 2020-04-23 16:51:09 KOROGLU_BA~ "@Ay~ tr    Azerba~  40.2 
##  4 744597~ 12533657~ 2020-04-23 16:51:05 FoodFocusSA "Pre~ en    South ~ -26.1 
##  5 155877~ 12533657~ 2020-04-23 16:51:01 opcionsecu~ "#AT~ es    Ecuador  -1.67
##  6 998960~ 12533657~ 2020-04-23 16:51:01 amystones4  "Tha~ en    United~  53.7 
##  7 102768~ 12533657~ 2020-04-23 16:51:00 COTACYT     "Men~ es    Mexico   23.7 
##  8 247382~ 12533657~ 2020-04-23 16:50:54 bkracing123 "The~ en    United~  53.9 
##  9 175662~ 12533657~ 2020-04-23 16:50:51 AnnStrahm   "Thi~ en    United~  37.5 
## 10 226707~ 12533656~ 2020-04-23 16:50:42 JLeonRojas  "INF~ es    Chile   -35.5 
## # ... with 18,214 more rows, and 1 more variable: lng <dbl>

covid_tweets_tidy <- covid_tweets %>% 
  filter(lang == "en") %>% # Selecting tweets only written in English
  mutate(hour = floor_date(created_at, unit="hour")) %>% # Creating a variable to aggregate tweets into the hour-long unit of time
  mutate(text = str_replace_all(text, "[#@]?[^[:ascii:]]+", " ")) %>% # Removing non-ASCII characters 
  mutate(text = str_replace_all(text, "&amp;|&lt;|&gt;|&quot;|RT", " ")) %>% # Removing HTML tags and retweet marker
  unnest_tweets(word, text) %>% # Splitting text into words by unnest_tweets 
  filter(!word %in% stopwords()) %>% # Removing words matched by any element in stopwords() vector
  filter(str_detect(word, "[a-z]")) # Selecting words that should contain any alphbetical letter

## Using `to_lower = TRUE` with `token = 'tweets'` may not preserve URLs.

covid_tweets_tidy

## # A tibble: 167,641 x 10
##    user_id status_id created_at          screen_name lang  country   lat   lng
##    <chr>   <chr>     <dttm>              <chr>       <chr> <chr>   <dbl> <dbl>
##  1 479491~ 12533658~ 2020-04-23 16:51:11 Vegastechh~ en    United~  36.0 -115.
##  2 479491~ 12533658~ 2020-04-23 16:51:11 Vegastechh~ en    United~  36.0 -115.
##  3 479491~ 12533658~ 2020-04-23 16:51:11 Vegastechh~ en    United~  36.0 -115.
##  4 479491~ 12533658~ 2020-04-23 16:51:11 Vegastechh~ en    United~  36.0 -115.
##  5 479491~ 12533658~ 2020-04-23 16:51:11 Vegastechh~ en    United~  36.0 -115.
##  6 479491~ 12533658~ 2020-04-23 16:51:11 Vegastechh~ en    United~  36.0 -115.
##  7 479491~ 12533658~ 2020-04-23 16:51:11 Vegastechh~ en    United~  36.0 -115.
##  8 479491~ 12533658~ 2020-04-23 16:51:11 Vegastechh~ en    United~  36.0 -115.
##  9 479491~ 12533658~ 2020-04-23 16:51:11 Vegastechh~ en    United~  36.0 -115.
## 10 479491~ 12533658~ 2020-04-23 16:51:11 Vegastechh~ en    United~  36.0 -115.
## # ... with 167,631 more rows, and 2 more variables: hour <dttm>, word <chr>

When text is formed into tidy data, we are ready to do sentiment analysis using inner_join

library(textdata)

covid_tweets_tidy %>% 
  inner_join(lexicon_bing()) # Joining with the bing lexicon

## Joining, by = "word"

## # A tibble: 15,936 x 11
##    user_id status_id created_at          screen_name lang  country   lat     lng
##    <chr>   <chr>     <dttm>              <chr>       <chr> <chr>   <dbl>   <dbl>
##  1 479491~ 12533658~ 2020-04-23 16:51:11 Vegastechh~ en    United~  36.0 -115.  
##  2 479491~ 12533658~ 2020-04-23 16:51:11 Vegastechh~ en    United~  36.0 -115.  
##  3 169480~ 12533658~ 2020-04-23 16:51:11 Coachjmorr~ en    United~  36.9  -81.1 
##  4 998960~ 12533657~ 2020-04-23 16:51:01 amystones4  en    United~  53.7   -1.65
##  5 998960~ 12533657~ 2020-04-23 16:51:01 amystones4  en    United~  53.7   -1.65
##  6 247382~ 12533657~ 2020-04-23 16:50:54 bkracing123 en    United~  53.9   -1.21
##  7 247382~ 12533657~ 2020-04-23 16:50:54 bkracing123 en    United~  53.9   -1.21
##  8 175662~ 12533657~ 2020-04-23 16:50:51 AnnStrahm   en    United~  37.5 -121.  
##  9 314986~ 12533656~ 2020-04-23 16:50:39 dande_hema~ en    India    15.9   80.8 
## 10 314986~ 12533656~ 2020-04-23 16:50:39 dande_hema~ en    India    15.9   80.8 
## # ... with 15,926 more rows, and 3 more variables: hour <dttm>, word <chr>,
## #   sentiment <chr>

covid_tweets_tidy %>% 
  inner_join(lexicon_nrc())

## Joining, by = "word"

## # A tibble: 57,217 x 11
##    user_id status_id created_at          screen_name lang  country   lat    lng
##    <chr>   <chr>     <dttm>              <chr>       <chr> <chr>   <dbl>  <dbl>
##  1 479491~ 12533658~ 2020-04-23 16:51:11 Vegastechh~ en    United~  36.0 -115. 
##  2 479491~ 12533658~ 2020-04-23 16:51:11 Vegastechh~ en    United~  36.0 -115. 
##  3 479491~ 12533658~ 2020-04-23 16:51:11 Vegastechh~ en    United~  36.0 -115. 
##  4 479491~ 12533658~ 2020-04-23 16:51:11 Vegastechh~ en    United~  36.0 -115. 
##  5 479491~ 12533658~ 2020-04-23 16:51:11 Vegastechh~ en    United~  36.0 -115. 
##  6 479491~ 12533658~ 2020-04-23 16:51:11 Vegastechh~ en    United~  36.0 -115. 
##  7 479491~ 12533658~ 2020-04-23 16:51:11 Vegastechh~ en    United~  36.0 -115. 
##  8 479491~ 12533658~ 2020-04-23 16:51:11 Vegastechh~ en    United~  36.0 -115. 
##  9 479491~ 12533658~ 2020-04-23 16:51:11 Vegastechh~ en    United~  36.0 -115. 
## 10 169480~ 12533658~ 2020-04-23 16:51:11 Coachjmorr~ en    United~  36.9  -81.1
## # ... with 57,207 more rows, and 3 more variables: hour <dttm>, word <chr>,
## #   sentiment <chr>

covid_tweets_tidy %>% 
  inner_join(lexicon_nrc_eil(), by=c("word"="term")) # Joining with the NRC-EIL by "word"="term". A character vector of variables to join by; using a variable with a common name across the two data sets; To join by a variable with different names, we specify that the variable "word" in covid_tweets_tidy is matched to the variable "term" in lexicon_nrc_eil().

## # A tibble: 23,895 x 12
##    user_id status_id created_at          screen_name lang  country   lat     lng
##    <chr>   <chr>     <dttm>              <chr>       <chr> <chr>   <dbl>   <dbl>
##  1 479491~ 12533658~ 2020-04-23 16:51:11 Vegastechh~ en    United~  36.0 -115.  
##  2 479491~ 12533658~ 2020-04-23 16:51:11 Vegastechh~ en    United~  36.0 -115.  
##  3 479491~ 12533658~ 2020-04-23 16:51:11 Vegastechh~ en    United~  36.0 -115.  
##  4 169480~ 12533658~ 2020-04-23 16:51:11 Coachjmorr~ en    United~  36.9  -81.1 
##  5 169480~ 12533658~ 2020-04-23 16:51:11 Coachjmorr~ en    United~  36.9  -81.1 
##  6 247382~ 12533657~ 2020-04-23 16:50:54 bkracing123 en    United~  53.9   -1.21
##  7 175662~ 12533657~ 2020-04-23 16:50:51 AnnStrahm   en    United~  37.5 -121.  
##  8 175662~ 12533657~ 2020-04-23 16:50:51 AnnStrahm   en    United~  37.5 -121.  
##  9 175662~ 12533657~ 2020-04-23 16:50:51 AnnStrahm   en    United~  37.5 -121.  
## 10 175662~ 12533657~ 2020-04-23 16:50:51 AnnStrahm   en    United~  37.5 -121.  
## # ... with 23,885 more rows, and 4 more variables: hour <dttm>, word <chr>,
## #   score <dbl>, AffectDimension <chr>

Counting sentiments by hour

After joining a tidy text data set with a sentiment lexicon, we can count the sentiment variable by the time variable

covid_tweets_tidy %>% 
  inner_join(lexicon_bing()) %>% 
  count(hour, sentiment) # Counting sentiments by hour

## Joining, by = "word"

## # A tibble: 40 x 3
##    hour                sentiment     n
##    <dttm>              <chr>     <int>
##  1 2020-04-22 21:00:00 negative    516
##  2 2020-04-22 21:00:00 positive    525
##  3 2020-04-22 22:00:00 negative    524
##  4 2020-04-22 22:00:00 positive    507
##  5 2020-04-22 23:00:00 negative    349
##  6 2020-04-22 23:00:00 positive    418
##  7 2020-04-23 00:00:00 negative    356
##  8 2020-04-23 00:00:00 positive    405
##  9 2020-04-23 01:00:00 negative    370
## 10 2020-04-23 01:00:00 positive    397
## # ... with 30 more rows

covid_tweets_tidy %>% 
  inner_join(lexicon_nrc()) %>% 
  count(hour, sentiment)

## Joining, by = "word"

## # A tibble: 200 x 3
##    hour                sentiment        n
##    <dttm>              <chr>        <int>
##  1 2020-04-22 21:00:00 anger          223
##  2 2020-04-22 21:00:00 anticipation   379
##  3 2020-04-22 21:00:00 disgust        179
##  4 2020-04-22 21:00:00 fear           384
##  5 2020-04-22 21:00:00 joy            280
##  6 2020-04-22 21:00:00 negative       577
##  7 2020-04-22 21:00:00 positive       720
##  8 2020-04-22 21:00:00 sadness        289
##  9 2020-04-22 21:00:00 surprise       201
## 10 2020-04-22 21:00:00 trust          462
## # ... with 190 more rows

covid_tweets_tidy %>% 
  inner_join(lexicon_nrc_eil(), by=c("word"="term")) %>% 
  count(hour, AffectDimension)

## # A tibble: 80 x 3
##    hour                AffectDimension     n
##    <dttm>              <chr>           <int>
##  1 2020-04-22 21:00:00 anger             237
##  2 2020-04-22 21:00:00 fear              467
##  3 2020-04-22 21:00:00 joy               536
##  4 2020-04-22 21:00:00 sadness           338
##  5 2020-04-22 22:00:00 anger             244
##  6 2020-04-22 22:00:00 fear              478
##  7 2020-04-22 22:00:00 joy               548
##  8 2020-04-22 22:00:00 sadness           330
##  9 2020-04-22 23:00:00 anger             146
## 10 2020-04-22 23:00:00 fear              320
## # ... with 70 more rows

Let’s visualize the time trend of sentiment in tweets toward COVID-19

library(ggplot2)
library(ggthemes)

covid_tweets_tidy %>% 
  inner_join(lexicon_bing()) %>% 
  count(hour, sentiment) %>% 
  ggplot(aes(x=hour, y=n, colour=sentiment)) +
  geom_line() +
  theme_bw() +
  labs(x = NULL, y = "Hourly Sum",
       title = "Tracing the rhythm of expressing sentiments toward COVID-19 on Twitter",
       subtitle = "The Bing Lexicon was used to measure sentiment in tweets")

## Joining, by = "word"

covid_tweets_tidy %>% 
  inner_join(lexicon_nrc()) %>% 
  count(hour, sentiment) %>% 
  ggplot(aes(x=hour, y=n, colour=sentiment)) +
  geom_line() +
  theme_bw() +
  labs(x = NULL, y = "Hourly Sum",
       title = "Tracing the rhythm of expressing sentiments toward COVID-19 on Twitter",
       subtitle = "The NRC Lexicon was used to measure sentiment in tweets")

## Joining, by = "word"

covid_tweets_tidy %>% 
  inner_join(lexicon_nrc_eil(), by=c("word"="term")) %>% 
  count(hour, AffectDimension) %>% 
  ggplot(aes(x=hour, y=n, colour=AffectDimension)) +
  geom_line() +
  theme_bw() +
  labs(x = NULL, y = "Hourly Sum",
       title = "Tracing the rhythm of expressing sentiments toward COVID-19 on Twitter",
       subtitle = "The NRC-EIL was used to measure sentiment in tweets")

What are the most common sentiment words used in the tweets?

We can analyze word counts that contribute to positive and negative sentiment in tweets. By implementing count() here with arguments of both word and sentiment, we find out how much each word contributed to each sentiment.

# Word count on tweets
covid_tweets_tidy %>% 
  inner_join(lexicon_bing()) %>% 
  count(sentiment, word, sort=TRUE) # Counting words by sentiments

## Joining, by = "word"

## # A tibble: 1,973 x 3
##    sentiment word         n
##    <chr>     <chr>    <int>
##  1 positive  like       460
##  2 positive  good       337
##  3 positive  work       316
##  4 positive  positive   301
##  5 negative  virus      282
##  6 positive  thank      255
##  7 positive  safe       234
##  8 positive  well       232
##  9 positive  trump      223
## 10 negative  crisis     212
## # ... with 1,963 more rows

covid_tweets_tidy %>% 
  inner_join(lexicon_bing()) %>% 
  count(sentiment, word, sort=TRUE) %>% 
  filter(sentiment=="positive") %>% 
  arrange(desc(n))

## Joining, by = "word"

## # A tibble: 751 x 3
##    sentiment word         n
##    <chr>     <chr>    <int>
##  1 positive  like       460
##  2 positive  good       337
##  3 positive  work       316
##  4 positive  positive   301
##  5 positive  thank      255
##  6 positive  safe       234
##  7 positive  well       232
##  8 positive  trump      223
##  9 positive  support    205
## 10 positive  great      187
## # ... with 741 more rows

covid_tweets_tidy %>% 
  inner_join(lexicon_bing()) %>% 
  count(sentiment, word, sort=TRUE) %>% 
  filter(sentiment=="negative") %>% 
  arrange(desc(n))

## Joining, by = "word"

## # A tibble: 1,222 x 3
##    sentiment word         n
##    <chr>     <chr>    <int>
##  1 negative  virus      282
##  2 negative  crisis     212
##  3 negative  death      197
##  4 negative  died       133
##  5 negative  hard        98
##  6 negative  infected    98
##  7 negative  lost        85
##  8 negative  die         84
##  9 negative  risk        83
## 10 negative  sick        83
## # ... with 1,212 more rows

The words like, positive, & trump are to be removed from the list of positive words because their meaning is not related to positive feelings in the context of COVID-19; And I also want to remove the word virus from the list of negative words because it is likely to be used in a way of indicating “Coronavirus”.

covid_tweets_tidy %>% 
  inner_join(lexicon_nrc()) %>% 
  count(sentiment, word, sort=TRUE) %>% 
  group_by(sentiment) %>% 
  top_n(10) %>% 
  arrange(sentiment, desc(n)) %>% 
  ungroup

## Joining, by = "word"

## Selecting by n

## # A tibble: 102 x 3
##    sentiment word          n
##    <chr>     <chr>     <int>
##  1 anger     fight       217
##  2 anger     death       197
##  3 anger     money       119
##  4 anger     fighting    115
##  5 anger     disease      85
##  6 anger     hit          73
##  7 anger     dying        72
##  8 anger     bad          69
##  9 anger     feeling      51
## 10 anger     challenge    44
## # ... with 92 more rows

The words virus in “negative”, don in “positive” and “trust”, trump in “surprise” are to be excluded from the analysis using the NRC lexicon.

covid_tweets_tidy %>% 
  inner_join(lexicon_nrc_eil(), by=c("word"="term")) %>% 
  count(AffectDimension, word) %>% 
  group_by(AffectDimension) %>% 
  top_n(10) %>% 
  arrange(AffectDimension, desc(n)) %>% 
  ungroup

## Selecting by n

## # A tibble: 40 x 3
##    AffectDimension word          n
##    <chr>           <chr>     <int>
##  1 anger           fight       217
##  2 anger           death       197
##  3 anger           money       119
##  4 anger           fighting    115
##  5 anger           disease      85
##  6 anger           hit          73
##  7 anger           dying        72
##  8 anger           bad          69
##  9 anger           feeling      51
## 10 anger           challenge    44
## # ... with 30 more rows

The words positive in “joy”, don in “positive” and “trust”, trump in “surprise” are to be excluded from the analysis using the NRC lexicon.

Creating a vector of words that need to be excluded from sentiment lexicons, i.e., positive, trump, etc.

words_out <- c("like", "positive", "trump", "virus", "don")

covid_tweets_tidy %>% 
  inner_join(lexicon_bing()) %>% 
  filter(!word %in% words_out) %>% 
  count(hour, sentiment) %>% 
  ggplot(aes(x=hour, y=n, colour=sentiment)) +
  geom_line() +
  theme_bw() +
  labs(x = NULL, y = "Hourly Sum",
       title = "Tracing the rhythm of expressing sentiments toward COVID-19 on Twitter",
       subtitle = "The Bing Lexicon was used to measure sentiment in tweets")

## Joining, by = "word"

covid_tweets_tidy %>% 
  inner_join(lexicon_nrc()) %>% 
  filter(!word %in% words_out) %>% 
  count(hour, sentiment) %>% 
  ggplot(aes(x=hour, y=n, colour=sentiment)) +
  geom_line() +
  theme_bw() +
  labs(x = NULL, y = "Hourly Sum",
       title = "Tracing the rhythm of expressing sentiments toward COVID-19 on Twitter",
       subtitle = "The NRC Lexicon was used to measure sentiment in tweets")

## Joining, by = "word"

covid_tweets_tidy %>% 
  inner_join(lexicon_nrc_eil(), by=c("word"="term")) %>% 
  filter(!word %in% words_out) %>% 
  count(hour, AffectDimension) %>% 
  ggplot(aes(x=hour, y=n, colour=AffectDimension)) +
  geom_line() +
  theme_bw() +
  labs(x = NULL, y = "Hourly Sum",
       title = "Tracing the rhythm of expressing sentiments toward COVID-19 on Twitter",
       subtitle = "The NRC-EIL was used to measure sentiment in tweets")

We can count the frequency of words in each sentiment type by tweet

covid_tweets_tidy %>% 
  inner_join(lexicon_bing()) %>% 
  filter(!word %in% words_out) %>% 
  count(hour, status_id, sentiment)

## Joining, by = "word"

## # A tibble: 8,929 x 4
##    hour                status_id           sentiment     n
##    <dttm>              <chr>               <chr>     <int>
##  1 2020-04-22 21:00:00 1253067601535815680 negative      5
##  2 2020-04-22 21:00:00 1253067601535815680 positive      1
##  3 2020-04-22 21:00:00 1253067622519681024 negative      1
##  4 2020-04-22 21:00:00 1253067629759209472 negative      2
##  5 2020-04-22 21:00:00 1253067638491807748 negative      2
##  6 2020-04-22 21:00:00 1253067644388982785 negative      1
##  7 2020-04-22 21:00:00 1253067644388982785 positive      3
##  8 2020-04-22 21:00:00 1253067656690839552 positive      4
##  9 2020-04-22 21:00:00 1253067673103204353 positive      1
## 10 2020-04-22 21:00:00 1253067716015071232 positive      1
## # ... with 8,919 more rows

Visualize the net score (positive - negative) using the “Bing” lexicon

*Let’s consider how we can calculate the net score of sentiment by tweet: Sum of positive words minus sum of negative words in each tweet

*To do so, we need to have two separate columns for positive and negative scores

*And, there will be also some days with no emotional words in tweets

*So, we will use spread() from tidyr package

`spread()` takes three principal arguments:

the data
the key column variable whose values will become new column names.
the value column variable whose values will fill the new column variables.

Spread() function

This yields a frequency table where the observations of sentiment for each tweet are spread across multiple rows, 9,559 observations from 7,203 tweets of 4 variables (hour,status_id, sentiment, n)

library(tibble)

covid_tweets_tidy %>% 
  inner_join(lexicon_bing()) %>% 
  count(hour, status_id, sentiment)

## Joining, by = "word"

## # A tibble: 9,559 x 4
##    hour                status_id           sentiment     n
##    <dttm>              <chr>               <chr>     <int>
##  1 2020-04-22 21:00:00 1253067601535815680 negative      5
##  2 2020-04-22 21:00:00 1253067601535815680 positive      1
##  3 2020-04-22 21:00:00 1253067622519681024 negative      1
##  4 2020-04-22 21:00:00 1253067629759209472 negative      2
##  5 2020-04-22 21:00:00 1253067638491807748 negative      2
##  6 2020-04-22 21:00:00 1253067644388982785 negative      1
##  7 2020-04-22 21:00:00 1253067644388982785 positive      3
##  8 2020-04-22 21:00:00 1253067656690839552 positive      4
##  9 2020-04-22 21:00:00 1253067673103204353 positive      1
## 10 2020-04-22 21:00:00 1253067716015071232 positive      1
## # ... with 9,549 more rows

Using spread() to key on sentiment with values from n, this becomes 7,203 observations of 4 variables (hour,status_id, negative, positive)

library(tidyr)
covid_tweets_tidy %>% 
  inner_join(lexicon_bing()) %>% 
  count(hour, status_id, sentiment) %>% 
  spread(key=sentiment, value=n, fill = 0)

## Joining, by = "word"

## # A tibble: 7,203 x 4
##    hour                status_id           negative positive
##    <dttm>              <chr>                  <dbl>    <dbl>
##  1 2020-04-22 21:00:00 1253067601535815680        5        1
##  2 2020-04-22 21:00:00 1253067622519681024        1        0
##  3 2020-04-22 21:00:00 1253067629759209472        2        0
##  4 2020-04-22 21:00:00 1253067638491807748        2        0
##  5 2020-04-22 21:00:00 1253067644388982785        1        3
##  6 2020-04-22 21:00:00 1253067656690839552        0        4
##  7 2020-04-22 21:00:00 1253067673103204353        0        1
##  8 2020-04-22 21:00:00 1253067716015071232        0        1
##  9 2020-04-22 21:00:00 1253067749305323525        2        1
## 10 2020-04-22 21:00:00 1253067782016688128        0        1
## # ... with 7,193 more rows

Calculate the net score of sentiment by positive score minus negative score

covid_tweets_tidy %>% 
  inner_join(lexicon_bing()) %>% 
  filter(!word %in% words_out) %>% 
  count(hour, status_id, sentiment) %>% 
  spread(key=sentiment, value=n, fill = 0) %>% 
  mutate(sentiment = positive - negative)

## Joining, by = "word"

## # A tibble: 6,866 x 5
##    hour                status_id           negative positive sentiment
##    <dttm>              <chr>                  <dbl>    <dbl>     <dbl>
##  1 2020-04-22 21:00:00 1253067601535815680        5        1        -4
##  2 2020-04-22 21:00:00 1253067622519681024        1        0        -1
##  3 2020-04-22 21:00:00 1253067629759209472        2        0        -2
##  4 2020-04-22 21:00:00 1253067638491807748        2        0        -2
##  5 2020-04-22 21:00:00 1253067644388982785        1        3         2
##  6 2020-04-22 21:00:00 1253067656690839552        0        4         4
##  7 2020-04-22 21:00:00 1253067673103204353        0        1         1
##  8 2020-04-22 21:00:00 1253067716015071232        0        1         1
##  9 2020-04-22 21:00:00 1253067749305323525        2        0        -2
## 10 2020-04-22 21:00:00 1253067791227392002        0        1         1
## # ... with 6,856 more rows

# Assigning each tweet with either positive or negative sentiment by the net score
covid_tweets_tidy %>% 
  inner_join(lexicon_bing()) %>% 
  filter(!word %in% words_out) %>% 
  count(hour, status_id, sentiment) %>% 
  spread(key=sentiment, value=n, fill = 0) %>% 
  mutate(sentiment = positive - negative) %>% 
  mutate(sentiment = ifelse(sentiment > 0, "Positive",
                            ifelse(sentiment < 0, "Negative", "Neutral")))

## Joining, by = "word"

## # A tibble: 6,866 x 5
##    hour                status_id           negative positive sentiment
##    <dttm>              <chr>                  <dbl>    <dbl> <chr>    
##  1 2020-04-22 21:00:00 1253067601535815680        5        1 Negative 
##  2 2020-04-22 21:00:00 1253067622519681024        1        0 Negative 
##  3 2020-04-22 21:00:00 1253067629759209472        2        0 Negative 
##  4 2020-04-22 21:00:00 1253067638491807748        2        0 Negative 
##  5 2020-04-22 21:00:00 1253067644388982785        1        3 Positive 
##  6 2020-04-22 21:00:00 1253067656690839552        0        4 Positive 
##  7 2020-04-22 21:00:00 1253067673103204353        0        1 Positive 
##  8 2020-04-22 21:00:00 1253067716015071232        0        1 Positive 
##  9 2020-04-22 21:00:00 1253067749305323525        2        0 Negative 
## 10 2020-04-22 21:00:00 1253067791227392002        0        1 Positive 
## # ... with 6,856 more rows

# ifelse(test, yes, no) returns a value with the same shape as test which is filled with elements selected from either yes or no depending on whether the element of test is TRUE or FALSE.

covid_tweets_bing <- covid_tweets_tidy %>% 
  inner_join(lexicon_bing()) %>% 
  filter(!word %in% words_out) %>% 
  count(hour, status_id, sentiment) %>% 
  spread(key=sentiment, value=n, fill = 0) %>% 
  mutate(sentiment = positive - negative) %>% 
  mutate(sentiment = ifelse(sentiment > 0, "Positive",
                            ifelse(sentiment < 0, "Negative", "Neutral")))

## Joining, by = "word"

Time-series visualization of sentiment analysis using “Bing” lexicon

# Now we are going to plot these net sentiment scores across hour-long bins. Note that we are plotting against the hour variable on the x-axis that keeps track of posted time in tweets
covid_tweets_bing %>% 
  count(hour, sentiment) %>% 
  ggplot(aes(x=hour,y=n, colour=sentiment)) +
  geom_line() +
  theme_bw() +
  labs(x = NULL, y = "Hourly Sum of Tweets",
       title = "Tracing the rhythm of expressing sentiments toward COVID-19 on Twitter",
       subtitle = "The Bing Lexicon was used to measure sentiment in tweets")

Let’s compare with the overall sentiment toward COVID-19, where all sentiment words on Twitter are aggregated into the hour unit

covid_tweets_tidy %>% 
  inner_join(lexicon_bing()) %>% 
  filter(!word %in% words_out) %>% 
  count(hour, sentiment) %>% 
  ggplot(aes(x=hour, y=n, colour=sentiment)) +
  geom_line() +
  theme_bw() +
  labs(x = NULL, y = "Hourly Sum",
       title = "Tracing the rhythm of expressing sentiments toward COVID-19 on Twitter",
       subtitle = "The Bing Lexicon was used to measure sentiment in tweets")

## Joining, by = "word"

W11: Word and Text Relatedness

Shin Lee

2020 5 21

Ch10. Word and text relatedness

Learning Objectives

Word Relatedness

Theoretical Foundations

Corpus-based Measures of Relatedness

Pointwise Mutual Information

Latent Semantic Analysis

Word Embeddings

R Practice for Tweet Sentiment Classification

Lexicon-based Sentiment Analysis III

Let’s preprocess tweet text

Counting sentiments by hour

What are the most common sentiment words used in the tweets?

Creating a vector of words that need to be excluded from sentiment lexicons, i.e., positive, trump, etc.

We can count the frequency of words in each sentiment type by tweet

Visualize the net score (positive - negative) using the “Bing” lexicon

`spread()` takes three principal arguments:

Calculate the net score of sentiment by positive score minus negative score

Time-series visualization of sentiment analysis using “Bing” lexicon

Let’s compare with the overall sentiment toward COVID-19, where all sentiment words on Twitter are aggregated into the hour unit

W11: Word and Text Relatedness

Shin Lee

2020 5 21

Ch10. Word and text relatedness

Learning Objectives

Word Relatedness

Theoretical Foundations

Corpus-based Measures of Relatedness

Pointwise Mutual Information

Latent Semantic Analysis

Word Embeddings

R Practice for Tweet Sentiment Classification

Lexicon-based Sentiment Analysis III

Let’s preprocess tweet text

Counting sentiments by hour

What are the most common sentiment words used in the tweets?

Creating a vector of words that need to be excluded from sentiment lexicons, i.e., positive, trump, etc.

We can count the frequency of words in each sentiment type by tweet

Visualize the net score (positive - negative) using the “Bing” lexicon

spread() takes three principal arguments:

Calculate the net score of sentiment by positive score minus negative score

Time-series visualization of sentiment analysis using “Bing” lexicon

Let’s compare with the overall sentiment toward COVID-19, where all sentiment words on Twitter are aggregated into the hour unit

`spread()` takes three principal arguments: