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Prediction of Individual's Character in Social Media Using Contextual Semantic Sentiment Analysis

机译：使用上下文语义情感分析预测社交媒体中的人格

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摘要

Sentiment analysis on social media has become most popular due to its extensive applications in both public and private sectors. We use twitter to know people's opinion towards any topic. Predicting character of an individual is important for any organization or society. Maslow hierarchy based prediction helps to define characteristic of the people. In this research, tweets are used to classify social media users based on Maslow hierarchy. We apply contextual semantic sentiment analysis to examine the people's character based on his/her tweets. In this research paper, three methods are proposed such as Opinion COW (Opinion Co-Occurrence Word) method, Opinion Circle method and Hybrid method to evaluate the tweets. We have recommended a new technique called opinion circle for sentiment analysis on tweets. Opinion circle method takes into account the co-occurrence words (contextual semantic) along with the Maslow keywords to capture the polarity of the tweet. Using opinion circle method, prior sentiment of the tweets may flip (positive to negative, positive to neutral or vice versa) due to the co-occurrence word. Our result shows that 51.46% of tweets flipping their sentiment because of co-occurrence word.
机译：由于社交媒体上的情感分析已在公共和私营部门中广泛应用，因此已成为最受欢迎的方法。我们使用Twitter了解人们对任何主题的看法。预测个人的品格对任何组织或社会都很重要。基于Maslow层次结构的预测有助于定义人的特征。在这项研究中，推文用于基于Maslow层次结构对社交媒体用户进行分类。我们应用上下文语义情感分析来基于人们的推文来检查人们的性格。本文提出了三种方法：意见共生词（Opinion CoWence Word）方法，意见圆法（Opinion Circle method）和混合方法（Hybrid method）来评估推文。我们推荐了一种称为意见圈的新技术，用于对推文进行情感分析。意见圈方法考虑了共现词（上下文语义）以及Maslow关键字来捕获推文的极性。使用意见圈方法，由于同时出现单词，推文的先前情感可能会翻转（从正到负，从正到中或反之亦然）。我们的结果显示，有51.46％的推文由于同时出现而使他们的情绪发生变化。

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