URWF: user reputation based weightage framework for twitter micropost classification

Abstract

Sentiment analysis is an emerging field that helps in understanding the sentiments of users on microblogging sites. Many sentiment analysis techniques have been proposed by researchers that classify and analyze the sentiments from micropost posted by various users. Majorly, these techniques perform text based classification that does not allow predicting the micropost impact. Further, it is very difficult to analyze this huge volume of online content produced each day. Therefore, an effective technique for sentiment analysis is required that not only performs the precise text-based classification but also makes the analysis easy by reducing the volume of data. Moreover, micropost impact must also be determined in order to segregate the high impact microposts in corpus. In the present study, we have presented sentiment analysis framework that incorporates any text based classification and separates out the high impact microposts from low impact by calculating the factor of user reputation. This user reputation is calculated by considering multiple factors regarding user activities that may help organizations to know customer opinions and views about their products and services. This way, volume of data becomes small that has to be analyzed by considering only microposts posted by high impact users. Multiple text classifications classes are introduced instead of just positive, negative and neutral for precise sentiment classification. The proposed framework also calculates the accumulated weight of each micropost by multiplying the user reputation with the assigned sentiment score. The user reputation calculation factors are validated by using Spearman rho and Kendall tau correlation coefficient. The framework is further evaluated by using the Sanders topic based corpus and results are presented.