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首页> 外文期刊>Applied Intelligence: The International Journal of Artificial Intelligence, Neural Networks, and Complex Problem-Solving Technologies >Successes and challenges in developing a hybrid approach to sentiment analysis
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Successes and challenges in developing a hybrid approach to sentiment analysis

机译：发展杂交途径对情感分析的成果及挑战

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摘要

This article covers some success and learning experiences attained during the developing of a hybrid approach to Sentiment Analysis (SA) based on a Sentiment Lexicon, Semantic Rules, Negation Handling, Ambiguity Management and Linguistic Variables. The proposed hybrid method is presented and applied to two selected datasets: Movie Review and Sentiment Twitter datasets. The achieved results are compared against those obtained when Na?ve Bayes (NB) and Maximum Entropy (ME) supervised machine learning classification methods are used for the same datasets. The proposed hybrid system attained higher accuracy and precision scores than NB and ME, which shows its superiority when applied to the SA problem at the sentence level. Finally, an alternative strategy to calculating the orientation polarity and polarity intensity in one step instead of the two steps method used in the hybrid approach is explored. The analysis of the yielded mixed results achieved with this alternative approach shows its potential as an aid in the computation of semantic orientations and produced some lessons learnt in developing a more effective mechanism to calculating the orientation polarity and polarity intensity.
机译：本文涵盖了在基于情感词典，语义规则，否定处理，歧义管理和语言变量的情绪分析（SA）的混合方法中实现了一些成功和学习体验。提出并应用于两个所选数据集：电影审查和情绪Twitter数据集。将达到的结果与当Na ve贝叶斯（NB）和最大熵（ME）监督机器学习分类方法用于同一数据集时比较。所提出的混合系统高于Nb和Me的精度和精确度，这在句子水平应用于SA问题时显示其优越性。最后，探讨了在一个步骤中计算定向极性和极性强度的替代策略而不是混合方法中使用的两个步骤方法。通过这种替代方法实现的产生的混合结果的分析表明其潜力作为对语义取向计算的辅助，并在开发更有效的机制以计算取向极性和极性强度来产生一些经验教训。

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