Analysis of Ensemble Classification of Twitter Sentiments Using New Dependency Tree Based Approach

The vast amount of data available on social media and microblogs can be a valuable resource for mining opinions or for analyzing the overall mood of the public. This helps in identifying potential customers, exploring market trends and predicting events. Analyzing twitter data is comparatively difficult due to the large amount of irregularities present in tweets. Many approaches that use sentiment dictionaries and machine learning have been proposed until now. In this paper, we present a new feature that is extracted using dependency parsing and an emotion lexicon. This feature, along with n-grams, syntactic n-grams and lexicon-based features, is used to classify the tweets. We also use custom dictionaries to identify slang words, SMS short forms, emoticons and word contractions. The performance of various classification algorithms and ensemble techniques is compared. Our results show that the new feature along with the ensemble framework improves sentiment classification.