Hey there! Let's tackle your two key challenges with Twitter sentiment analysis for Donald Trump's tweets—fixing your underperforming Naive Bayes classifier, and refining how you filter tweets for better results.

There are a few common reasons your model isn't hitting higher accuracy, and easy fixes to try:

• Your training dataset is too small
  100 labeled tweets is barely enough for a sentiment analysis model to learn meaningful patterns. Naive Bayes, in particular, relies on having enough examples to capture word-sentiment correlations without overfitting.
  Fix: Expand your labeled dataset. You can either:

  • Manually label more tweets (aim for at least 500+ for better results)
  • Use publicly available labeled datasets focused on political or Trump-related sentiment
  • Try semi-supervised learning: use your current model to predict sentiment on unlabeled tweets, then manually review the highest-confidence predictions to add to your training set.

• TextBlob's default preprocessing isn't optimized for Twitter
  Twitter text is messy—full of @mentions, hashtags, emojis, slang, and links. TextBlob's out-of-the-box processing doesn't handle these nuanced elements well, so your model is missing critical sentiment signals.
  Fix: Add custom preprocessing steps before feeding text to the classifier:

  • Strip or normalize @usernames and hashtags (or keep hashtags if they're sentiment-rich, like #MAGA)
  • Convert emojis to text descriptors (e.g., 😊 → "happy", 😡 → "angry")
  • Remove HTTP links and special characters
  • Expand contractions (use libraries like contractions to turn "don't" into "do not")
  • Filter out stopwords (or test leaving them in—Naive Bayes can sometimes benefit from retaining common context words)

• Naive Bayes' independence assumption doesn't fit Twitter text
  Naive Bayes assumes all features (words) are independent, but Twitter is full of phrase-based sentiment (e.g., "Crooked Hillary" is a loaded phrase that can't be split into individual words). This breaks the model's core assumption.
  Fix:

  • Use n-grams (bigrams/trigrams) as features instead of just single words. In TextBlob, you can adjust the ngram_range parameter when vectorizing text.
  • Try switching to a more robust model like Logistic Regression or SVM—these handle feature dependencies better than Naive Bayes for text tasks.

• Labeling inconsistencies might be throwing off the model
  Your example NEG tweet has a sarcastic line ("Will be such fun!") that could be mislabeled if you aren't careful. Sarcasm and irony are rampant in political Twitter, and inconsistent labeling will confuse the model.
  Fix: Audit your labeled dataset to ensure every tweet's sentiment is clearly defined. Pay extra attention to sarcastic, satirical, or context-dependent content—these need precise labeling.

To get high-quality data that helps your model learn better, use these filtering strategies:

• Target relevant content
  Focus on tweets that explicitly mention Donald Trump (e.g., keywords like "Donald Trump", "Trump", "@realDonaldTrump"). Exclude tweets that only reference him in a neutral, factual way (e.g., "Donald Trump turned 77 in June")—these don't add useful sentiment data.

• Remove noise

  • Duplicate tweets: Bots often repost content, so filter out exact duplicates to avoid skewing your dataset.
  • Short/empty tweets: Tweets with fewer than 3-5 words rarely have clear sentiment (unless they're obvious like "Trump sucks!"), so exclude them.
  • Advertising/link-heavy tweets: Tweets that are mostly links or promotional content lack genuine sentiment—cut these out.

• Prioritize original content
  Skip retweets unless you're specifically analyzing how users feel about sharing Trump-related content. Original tweets reflect the author's direct sentiment, which is what you want for most sentiment analysis tasks.

• Filter by language and context

  • Stick to English tweets (use the lang="en" filter if you're using the Twitter API or a dataset with language tags) to avoid cross-language noise.
  • If you're analyzing specific events (e.g., a rally, a tweet from Trump), filter tweets by timestamp to focus on that context.

• Handle reply tweets carefully
  Reply tweets often reference another user's content, so their sentiment might depend on the original tweet. If you're doing standalone sentiment analysis, exclude replies; if you want to analyze context-dependent sentiment, include the original tweet text alongside the reply.

内容的提问来源于stack exchange，提问作者Ala Głowacka