On predicting elections with hybrid topic based sentiment analysis of tweets
Barkha Bansal, Sangeet Srivastava · Procedia Computer Science · 2018
Twitter sentiment analysis is quick and inexpensive way for real-time election monitoring and modern day election predictions. Recent research relies on explicit mining of public sentiment using lexical and syntactic features in tweets. However, underlying implicit word relations and co-occurrences are overlooked. This task of capturing semantic relations and word co-occurrences further becomes challenging in case of short length tweets where words are limited. In this paper, we introduce a novel method: Hybrid Topic Based Sentiment Analysis (HTBSA) with the aim of capturing word relations and co-occurrences in short length tweets for election prediction using tweets. First, we extract latent topics from rich corpus of short texts using Biterm Topic model (BTM), then sentiments for each topic are learnt from pre-existing lexical resources. Finally, sentiment score of each tweet is calculated using sentiment orientation and weight of each topic contained in it. We use more than 300,000 tweets, collected from 1st-20th February, 2017, to predict Uttar Pradesh (U.P) legislative elections. Geo tagging is employed for key words which are not exclusive to the elections. Results show that HTBSA has out performed existing Twitter based election prediction techniques with a decrease of 3.5% in MAE. Our study can be easily and efficiently extended for real time election monitoring and future election predictions.