An improved lexicon using logistic regression for sentiment analysis

摘要

Recently, stock market activities are becoming dependent intensely on social media interactions to deliver significant information for an extensive number of users. This obliges frameworks to scale expeditiously to suit the surge of new as well as existing users going to the recommendations based on information extracted from social media. In this work, we proposed an approach for assigning scores to lexicon items using logistic regression based relative scoring to address both the proposal quality and the framework versatility. We proposed to assigns a rich range of scores to items in the lexicon, as indicated by their web usage history and corresponding effects. Most of the lexicon acquisition frameworks regard words as paired vectors under the exemplary sack of-words model; however, there is not an idea of relative comparability between words while depicting the same sentiment effect. This relativity is considered using the logistic regression model and the accuracy of the results is found to be improved significantly.