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.

随着社交媒体的迅猛发展，互联网的用户数据库正在迅速扩展。这些信息包括有关产品，想法，新闻，政治等方面的信息以及个人意见。这些在线意见和评论可作为口口相传的媒介，用于增强或减少产品，物品或概念的受欢迎程度。因此，对在线意见的语气进行自动分析有助于客户和业务人员有效地做出决策和制定策略。这项任务被称为情感分析，是一个活跃的研究领域，在很大程度上依赖于称为词嵌入的文本处理方法。词嵌入是将文本表示为数字格式的过程，以对其进行数学运算。本研究提出了一种增强词嵌入方法性能的方法，通过集成基于情感的信息，使其更适合情感分析。基于情感的信息是通过自组织图合并而成的，其中基于基于情感的单词的分数来计算相似度。使用粒子群优化方法进一步调整相似性。实验上，使用各种分类器证明了所提出方法的性能对于情感分析任务是合理的。与现有方法相比，使用不同的性能测量指标来验证所提出方法的优越性。使用粒子群优化方法进一步调整相似性。实验上，使用各种分类器证明了所提出方法的性能对于情感分析任务是合理的。与现有方法相比，使用不同的性能测量指标来验证所提出方法的优越性。使用粒子群优化方法进一步调整相似性。实验上，使用各种分类器证明了所提出方法的性能对于情感分析任务是合理的。与现有方法相比，使用不同的性能测量指标来验证所提出方法的优越性。