Which bigram association measures are more suitable for sentiment analysis? Why?

For sentiment analysis, the Pointwise Mutual Information (PMI) measure is often considered more suitable than other bigram association measures. Here's why:

Captures Specific Associations: PMI is effective at capturing specific word associations that are indicative of sentiment. It measures the degree of association between a pair of words by comparing their observed frequency with the expected frequency under independence. In sentiment analysis, identifying meaningful and statistically significant word associations is crucial for accurately determining sentiment.
Handles Sparse Data: Sentiment analysis datasets often contain sparse data, where certain sentiment-related bigrams may have low frequencies. PMI performs well even with sparse data by considering the joint probability and individual word probabilities. It can highlight rare but highly informative word associations that might be missed by other measures.
Balanced Handling of Positive and Negative Associations: PMI is capable of capturing both positive and negative associations between words. In sentiment analysis, it's essential to identify both positive and negative sentiment indicators accurately. PMI's ability to handle associations in both directions makes it suitable for sentiment analysis tasks.
Well-Established and Widely Used: PMI is a well-established and widely used bigram association measure in the field of natural language processing. Its effectiveness and interpretability have been demonstrated in various applications, including sentiment analysis. Many sentiment analysis frameworks and libraries leverage PMI to identify sentiment-bearing word pairs.

While PMI is often preferred for sentiment analysis, it's worth noting that the choice of association measure can depend on the specific characteristics of the dataset and the particular sentiment analysis task at hand. It is always recommended to experiment and evaluate different measures to determine the most suitable one for a given scenario.