使用BERT进行电影评论情感分析
Sentiment Analysis of Movie Reviews Using BERT
AI总结：
本文通过微调BERT与BiLSTM进行电影评论情感分析，旨在超越现有最先进方法，并利用启发式算法计算总体极性以支持电影推荐。
摘要：
情感分析（SA）或意见挖掘是对任何类型文本中情感和意见的分析。SA有助于追踪人们的观点，并且在社交媒体监控、产品和品牌识别、客户满意度、客户忠诚度、广告和促销成功以及产品接受度方面是一个重要因素。这就是为什么SA是自然语言处理（NLP）中活跃的研究领域之一。SA应用于从各种媒体平台获取的数据，以从中挖掘情感知识。文献中已经部署了各种方法来解决这个问题。大多数技术设计了复杂而精细的框架以达到最佳准确性。这项工作旨在微调双向编码器表示（BERT）与双向长短期记忆（BiLSTM）用于电影评论情感分析，并提供比最先进（SOTA）方法更好的准确性。本文还展示了如何应用情感分析，例如，如果有人想推荐某部电影，可以通过计算模型预测的情感总体极性来实现。也就是说，我们提出的方法作为预测对电影的主要反应的基线。为了计算总体极性，对BERTBiLSTM输出向量应用了一种启发式算法。我们的模型可以扩展到三类、四类或任何细粒度分类，并再次应用总体极性计算。这旨在未来的工作中加以利用。
英文摘要：
Sentiment Analysis (SA) or opinion mining is analysis of emotions and opinions from any kind of text. SA helps in tracking peoples viewpoints and it is an important factor when it comes to social media monitoring product and brand recognition customer satisfaction customer loyalty advertising and promotions success and product acceptance. That is why SA is one of the active research areas in Natural Language Processing (NLP). SA is applied on data sourced from various media platforms to mine sentiment knowledge from them. Various approaches have been deployed in the literature to solve the problem. Most techniques devise complex and sophisticated frameworks in order to attain optimal accuracy. This work aims to finetune Bidirectional Encoder Representations from Transformers (BERT) with Bidirectional Long Short-Term Memory (BiLSTM) for movie reviews sentiment analysis and still provide better accuracy than the State-of-The-Art (SOTA) methods. The paper also shows how sentiment analysis can be applied if someone wants to recommend a certain movie for example by computing overall polarity of its sentiments predicted by the model. That is our proposed method serves as an upper-bound baseline in prediction of a predominant reaction to a movie. To compute overall polarity a heuristic algorithm is applied to BERTBiLSTM output vector. Our model can be extended to three-class four-class or any fine-grained classification and apply overall polarity computation again. This is intended to be exploited in future work.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
