Integrating Multiple Data Sources to Enhance Sentiment Prediction

Understanding the sentiment conveyed by a person is an important part of any social interaction, and sentiment in text can provide valuable insight into an author's opinion. Sentiment analysis for text is a large field of research within machine learning, as it allows the sentiment of large numbers of text instances to be determined and used to answer various questions, such as election prediction. Typically, a sentiment classifier is trained using data from the same domain it is intended to be applied to; however, there may not be sufficient training data within the given domain. Additionally, using data from multiple sources, including other related domains, may help create a more generalized sentiment classifier that can be applied to multiple domains. To this aim, we conduct an empirical study using sentiment data from two sources, online reviews and tweets. We first test the performance of sentiment analysis models built using a single data source for both in-domain and cross-domain classification. Then, we evaluate classifiers trained using instances randomly sampled from both sources. Additionally, we evaluate sampling different quantities of instances from both data sources to determine how many instances should be included in a training data set. We apply statistical tests to verify the significance of our results and find that using a combination of instances from reviews and tweets is similar to, or better than any model trained from a single domain. Also, we found no significant difference in performance for classifiers 100,000 or more combined training instances. These results are important as they indicate a more robust classifier can be trained by using a smaller number of in-domain instances augmented with instances from a related domain, rather than using purely in-domain instances. Thus, we recommend using a training data set composed of both tweets and reviews, when training a sentiment classifier for use in predicting both tweet and review sentiment.