Introduction

The 2026 FIFA World Cup represents a major moment for soccer in North America, with matches hosted across the United States, Mexico, and Canada. For Los Angeles, a city not traditionally known for its strong attachment to professional soccer, the tournament has created a noticeable shift in public interest and enthusiasm. As the World Cup approaches, soccer appears to be gaining greater visibility in everyday life, especially through the growing presence of USA and Mexico fútbol jerseys seen throughout the city.

This project uses sentiment analysis to measure and better understand current public attitudes toward several major national teams competing in the 2026 FIFA World Cup. The analysis focuses on the top three international teams—France, Argentina, and Spain—as well as Mexico and the United States. These teams were selected because they represent a mix of global soccer powerhouses and countries with strong cultural and geographic relevance to Los Angeles.

Based on local observations, the sentiment toward Mexico and the United States appears to be overwhelmingly positive in 2026. The increasing visibility of team jerseys, public conversations, and overall excitement around the tournament suggest that the World Cup may be changing how soccer is perceived in Los Angeles. By analyzing public sentiment, this project aims to determine whether these observations are reflected in broader data and to compare how enthusiasm differs across the selected teams.

Sources

Headlines were collected from multiple media sources, then deduplicated and cleaned before conducting the sentiment word comparison.

Sample Cleaned Headlines
source_name title pub_day
Variety Autographed Lionel Messi Jerseys Drop Online, As Argentine Star Leads Team Into World Cup Knockout Round 2026-06-30
Get French Football News France predicted XI v Sweden: William Saliba and Bradley Barcola to return 2026-06-30
The Football Faithful Sweden XI vs France – Predicted lineup and team news 2026-06-30
The Football Faithful France vs Sweden – Predicted lineup and team news 2026-06-30
The Irish Times World Cup 2026: Germany have become an international Spurs 2026-06-30
Footballtoday.com Germany crash out as England fixture hit by attendance concerns 2026-06-30
Dailymail.com World Cup Breakfast: DOUBLE penalty drama - with all the highlights - and there’s tears for bereaved Cody Gakpo after scoring - plus what to look out for today as France return to action 2026-06-30
Fox Sports Is Erling Haaland Playing Today? Norway Star’s Status vs. Ivory Coast 2026-06-30
USA Today ‘An epic night’: Paraguay reflects on journey from loss to USA to upset win 2026-06-30
Sporting News Where to watch Ivory Coast vs. Norway live stream, TV channel, start time for World Cup Round of 32 match 2026-06-30

Sentiment Analysis

The Bing Lexicon was used to classify words from the collected headlines as either positive or negative and produce the top 10 for each category. Words such as “win” and “top” appeared frequently on the positive side, suggesting favorable coverage and strong team performance. However, some words were more difficult to interpret in context. For example, words like “upset” and “upsets” were classified as negative by the lexicon.

In sports reporting, however, the word “upset” does not always indicate negative sentiment. An upset usually means that a lower-ranked or less-favored team defeated a stronger opponent. While the word may sound negative in general language, it can represent an exciting or positive event for the winning team and its fans. This highlights one limitation of lexicon-based sentiment analysis: individual words may be classified without fully understanding the sports context in which they are used.

As a result, the sentiment results should be interpreted carefully. While the Bing Lexicon provides a useful starting point for comparing positive and negative language, some words may require manual review to determine whether they truly reflect positive or negative sentiment in the context of soccer headlines.

Interpretation: Words on the left panel are pulling overall sentiment down; words on the right are pulling it up. A marketing analyst would scan this chart for words tied to specific events (e.g., “crash,” “delay,” “explosion” vs. “record,” “success,” “win”) to understand what kind of news is driving the tone — not just whether the tone is positive or negative.

Sentiment Volume Comparison Across Topics

If you pulled multiple topics (brands), this chart compares how many positive vs. negative sentiment-words appear in each topic’s headlines.

Interpretation: A higher total bar (positive + negative) for a topic means that topic generated more emotionally-charged language overall — which could reflect either higher news volume or more dramatic events. Comparing the ratio of green to red bars across topics tells you which brand is currently enjoying more favorable framing in the media.

Top Words Overall

Independent of sentiment, it’s useful to see which words dominate the headlines overall.

Interpretation: This is your “what is everyone talking about” chart. Look for names of products, executives, partners, or events that recur — these are candidates for deeper investigation (e.g., is “Starship” appearing a lot because of a successful launch or a setback?).

The AFINN Lexicon (Scored -5 to +5)

Unlike Bing’s binary labels, AFINN assigns each word a numeric score from -5 (very negative) to +5 (very positive), allowing us to compute average sentiment intensity per topic.

AFINN Sentiment Score by Topic
source_name words_matched mean_sentiment sum_sentiment
Alloutsoccer.com 1 2.000 2
Fox Sports 9 0.889 8
USA Today 9 0.222 2
Crypto Briefing 4 0.000 0
Variety 1 -1.000 -1
HITC - Football, Gaming, Movies, TV, Music 3 -1.333 -4
MassLive.com 3 -1.667 -5
BBC News 1 -2.000 -2
Dailymail.com 3 -2.000 -6
Footballtoday.com 1 -2.000 -2
Managing Madrid 1 -2.000 -2
New York Post 1 -2.000 -2
Al Jazeera English 1 -3.000 -3

Interpretation: mean_sentiment tells you the average emotional tone of matched words for each topic — a value near 0 suggests neutral/mixed coverage, while a clearly positive or negative mean suggests a dominant tone. sum_sentiment reflects total emotional “weight,” which is influenced by both tone and volume of coverage.

Bing Sentiment Split (Positive vs. Negative Counts)

This table reshapes the Bing results into a wide format so you can directly compare positive counts, negative counts, and the net (positive − negative) score for each topic.

Bing Sentiment Count by Topic
source_name positive negative net
Alloutsoccer.com 1 0 1
BBC News 0 2 -2
Crypto Briefing 3 1 2
Dailymail.com 0 1 -1
Footballtoday.com 0 2 -2
Fox Sports 3 1 2
HITC - Football, Gaming, Movies, TV, Music 0 3 -3
Managing Madrid 0 1 -1
MassLive.com 0 2 -2
New York Post 1 0 1
U.S. Soccer 0 1 -1
USA Today 4 5 -1
Variety 1 0 1

Interpretation: The net column is a simple, interpretable sentiment index. A positive net score suggests headlines skew favorable; a negative net score suggests the opposite. This kind of index is easy to track over time (e.g., weekly) to build a brand sentiment trendline.

TF-IDF: What Makes Each Topic’s Coverage Distinct?

TF-IDF (Term Frequency–Inverse Document Frequency) identifies words that are frequent within one topic’s headlines but rare across other topics’ headlines. This is especially useful for understanding what makes coverage of one brand distinctive compared to another.

Refining the TF-IDF Plot for Many Topics

When you have more than two topics, the basic plot above can get crowded. The refined version below dynamically adjusts the color palette to the number of topics, reduces the number of terms shown per topic, truncates long words, and arranges panels in a grid for readability.

Interpretation: Words with high TF-IDF scores for a given topic are the terms that “define” that topic’s coverage relative to others — these are often product names, executives, locations, or event-specific terms. For a marketing analyst, these words are strong candidates for keyword tracking, campaign hashtag ideas, or identifying emerging narratives unique to your brand versus competitors.

Conclusion

The sentiment analysis provides insight into how media coverage is shaping public perception of selected teams in the 2026 FIFA World Cup. By collecting, cleaning, and deduplicating headlines from multiple sources, this project compared positive and negative sentiment words associated with France, Argentina, Spain, Mexico, and the United States.

Overall, the analysis suggests that World Cup coverage is generating strong interest and generally positive sentiment, especially around teams with high visibility in Los Angeles, such as Mexico and the United States. This supports the observation that soccer enthusiasm in the city has increased as the tournament approaches. The growing presence of USA and Mexico fútbol jerseys reflects a broader shift in local engagement with the sport.

At the same time, the results should be interpreted with some caution. Lexicon-based sentiment analysis is useful for identifying broad patterns, but it does not always understand context. Words such as “upset” may be classified as negative even when they describe an exciting sports outcome. Because of this, sentiment scores should be viewed as directional rather than absolute.

In conclusion, the 2026 FIFA World Cup appears to be creating a more positive and visible soccer culture in Los Angeles. The analysis shows that sentiment data can help measure this shift, compare team-level media coverage, and better understand how major sporting events influence public interest and fan engagement.