Examples of specific sentiment analysis tasks where PMI would be more effective than LLR or chi-square

The followings are a few examples of sentiment analysis tasks where Pointwise Mutual Information (PMI) could be more effective than Log-Likelihood Ratio (LLR) or Chi-square (χ²):

1. Fine-Grained Sentiment Analysis: In fine-grained sentiment analysis, the goal is to classify text into multiple sentiment categories, such as positive, negative, and neutral, or even more specific emotions like joy, sadness, anger, etc. PMI can be more effective in this scenario because it captures specific word associations that are indicative of sentiment. It can help identify sentiment-bearing bigrams that are highly informative for distinguishing between different sentiment categories.

2. Domain-Specific Sentiment Analysis: Sentiment analysis often needs to be tailored to specific domains, such as product reviews in e-commerce or social media discussions about movies. In these cases, PMI can be more effective as it captures domain-specific word associations that are relevant for sentiment analysis within that particular domain. By identifying associations specific to the domain, PMI can provide more accurate sentiment analysis results compared to LLR or χ².

3. Sentiment Lexicon Expansion: Sentiment lexicons are valuable resources for sentiment analysis, containing words and phrases mapped to their associated sentiment polarity. PMI can be useful for expanding sentiment lexicons by identifying new sentiment-bearing bigrams that are highly associated with sentiment categories. It can help uncover meaningful word associations that may not be captured by LLR or χ², contributing to the enrichment and improvement of sentiment lexicons.

4. Comparative Sentiment Analysis: Comparative sentiment analysis involves comparing sentiment between multiple entities or aspects within a text, such as comparing the sentiment towards different products in a set of reviews. PMI can be more effective in this scenario as it can identify specific word associations that indicate comparative sentiment. It can help uncover significant associations that indicate preference, comparison, or contrast between different entities or aspects.

It's important to note that the effectiveness of PMI, LLR, and χ² can still depend on the specific dataset, task requirements, and underlying characteristics of the text being analyzed. Therefore, it is recommended to experiment and evaluate different measures on your specific sentiment analysis task to determine the most effective approach for your particular scenario.