Sentiment Analysis Using Averaged Weighted Word Vector Features
This work addresses sentiment analysis for consumer decision-making by improving accuracy in classifying online reviews, though it appears incremental as it builds on existing word vector techniques.
The authors tackled sentiment analysis by developing two methods that combine word vectors with weighted averaging based on word frequencies in sensitivity-tagged reviews, and they reported that their approaches outperform state-of-the-art success rates on standard benchmark datasets.
People use the world wide web heavily to share their experience with entities such as products, services, or travel destinations. Texts that provide online feedback in the form of reviews and comments are essential to make consumer decisions. These comments create a valuable source that may be used to measure satisfaction related to products or services. Sentiment analysis is the task of identifying opinions expressed in such text fragments. In this work, we develop two methods that combine different types of word vectors to learn and estimate polarity of reviews. We develop average review vectors from word vectors and add weights to this review vectors using word frequencies in positive and negative sensitivity-tagged reviews. We applied the methods to several datasets from different domains that are used as standard benchmarks for sentiment analysis. We ensemble the techniques with each other and existing methods, and we make a comparison with the approaches in the literature. The results show that the performances of our approaches outperform the state-of-the-art success rates.