Improved method of word embedding for efficient analysis of human sentiments

User database of the internet is expanding at a swift rate with the dramatic growth of social media. These include information as well as personal opinions about products, ideas, news, politics, etc. These online opinions and reviews act as a word-to-mouth medium for enhancing or diminishing the popularity of a product, item or concept. Thus, automated analysis of the tone of online opinions helps customers and business personnel significantly to take decisions and develop strategies efficiently. This task, known as sentiment analysis, is an area of active research that relies heavily on the text processing methodology called word embedding. Word embedding is a process of representing text into numeric format, to enable mathematical operations on them. The present study proposes a method of enhancing the performance of word embedding approaches, by integrating sentiment-based information, to render them more suitable for sentiment analysis. Sentiment-based information is incorporated through self-organizing map, where similarity is calculated based on the scores of sentiment-based words. The similarity is further tuned using particle swarm optimization method. Experimentally, performance of the proposed method is justified for sentiment analysis task using various classifiers. Different performance measurement indexes are used to validate the superiority of the proposed method compared to existing approaches.