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首页> 外文期刊>Journal of medical systems >A Robust User Sentiment Biterm Topic Mixture Model Based on User Aggregation Strategy to Avoid Data Sparsity for Short Text
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A Robust User Sentiment Biterm Topic Mixture Model Based on User Aggregation Strategy to Avoid Data Sparsity for Short Text

机译：一种强大的用户情感比特妨据基于用户聚合策略的混合模型，以避免短文本的数据稀疏性

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摘要

Sentiment analysis is a process of computationally finding the opinions that are expressed in a short text or a feedback by a writer towards a particular topic, product, service. The short piece of review from the user can help a business determine or understand the attitude of the user thereby predict the customer's behaviour and itsubstantiallyimproves the quality of service parameters. The proposed Robust User Sentiment Biterm Topic Mixture (RUSBTM)model discovers the user preference and their sentiment orientation views for effective Topic Modelling using Biterms or word-pair from the short text of a particular venue. Since short review or text suffers from data sparse, the user aggregation strategy is adapted to form a pseudo document and the word pairset is created for the whole corpus. The RUSBTM learns topics by generating the word co-occurrence patterns thereby inferring topics with rich corpus-level information. By analysing the sentiments of the paired words and their corresponding topics in the review corpus of the particular venue, prediction can be done that exactly portrays the user interest, preference and expectation from a particular venue. The RUSBTM model proved to be more robust and also, the extracted topics are more coherent and informative. Also the method uses accurate sentiment polarity techniques to exactly capture the sentiment orientation and the model proves to be outperforming better when compared to other state of art methods.
机译：情感分析是计算方式计算在短文本中表达的意见或作者对特定主题，产品，服务的反馈。来自用户的短片审查可以帮助业务确定或理解用户的态度，从而预测客户的行为和itsubStantimpRovers服务质量参数。所提出的强大的用户情感BENERM主题混合（RUSBTM）模型发现用户偏好和他们的情感方向视图，以便使用比较特定场地的短文本的比特里斯或字对对的有效主题建模。由于简短的审查或文本遭受数据稀疏，因此用户聚合策略适于形成伪文档，并且为整个语料库创建单词Piapset。 RusBtm通过生成单词共同发生模式来学习主题，从而推断出具有丰富的语料库级信息的主题。通过分析特定场地的审查语料库中配对词的情绪及其相应的主题，可以进行预测，恰好描绘了来自特定场地的用户兴趣，偏好和期望。 RUSBTM模型被证明是更强大的，而且，提取的主题更加连贯和信息。此外，该方法使用精确的情绪极性技术来精确地捕捉情绪取向，与其他现实方法的状态相比，模型被证明更优于更好。

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