A Logistic Regression Approach for Generating Movies Reputation Based on Mining User Reviews

The paper aims to present an approach for generating a single reputation value towards a target movie based on mining movie reviews and their attached ratings with the use of Logistic Regression classifier and Latent Semantic Indexing (LSI) method. The contribution of the paper is fourfold. First, we apply Logistic Regression classifier to determine the sentiment orientation of movie reviews (positive or negative). Second, we use LSI method and cosine similarity to compute the semantic similarity between reviews. Third, we compute a custom reputation value separately for positive opinions group and negative opinions group. Finally, we use the weighted arithmetic mean to generate a single reputation value towards the target movie.

[1]  Yen-Liang Chen,et al.  Opinion mining from online hotel reviews - A text summarization approach , 2017, Inf. Process. Manag..

[2]  Andrea Esuli,et al.  Revisiting Distributional Correspondence Indexing: A Python Reimplementation and New Experiments , 2018, ArXiv.

[3]  Luyao Huang,et al.  Utilizing BERT for Aspect-Based Sentiment Analysis via Constructing Auxiliary Sentence , 2019, NAACL.

[4]  Christopher Potts,et al.  Learning Word Vectors for Sentiment Analysis , 2011, ACL.

[5]  Witold Pedrycz,et al.  Fusing and mining opinions for reputation generation , 2017, Inf. Fusion.

[6]  Ming-Wei Chang,et al.  BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding , 2019, NAACL.

[7]  El Habib Nfaoui,et al.  A Conditional Sentiment Analysis Model for the Embedding Patient Self-report Experiences on Social Media , 2018 .

[8]  Wu He,et al.  Comparing consumer-produced product reviews across multiple websites with sentiment classification , 2018, J. Organ. Comput. Electron. Commer..

[9]  Lei Zhang,et al.  Sentiment Analysis and Opinion Mining , 2017, Encyclopedia of Machine Learning and Data Mining.

[10]  Tatsuya Harada,et al.  Asymmetric Tri-training for Unsupervised Domain Adaptation , 2017, ICML.

[11]  George A. Miller,et al.  WordNet: A Lexical Database for English , 1995, HLT.

[12]  El Habib Nfaoui,et al.  A hybrid approach for generating reputation based on opinions fusion and sentiment analysis , 2019, J. Organ. Comput. Electron. Commer..

[13]  Shiliang Sun,et al.  A review of optimization methodologies in support vector machines , 2011, Neurocomputing.

[14]  A. Choudhary,et al.  Mining millions of reviews: a technique to rank products based on importance of reviews , 2011, ICEC '11.

[15]  Shiliang Sun,et al.  A review of natural language processing techniques for opinion mining systems , 2017, Inf. Fusion.

[16]  Benxiong Huang,et al.  An approach to rank reviews by fusing and mining opinions based on review pertinence , 2015, Inf. Fusion.

[17]  D VelásquezJuan,et al.  Opinion Mining and Information Fusion , 2016 .

[18]  Kevin Gimpel,et al.  ALBERT: A Lite BERT for Self-supervised Learning of Language Representations , 2019, ICLR.

[19]  Erik Cambria,et al.  Aspect extraction for opinion mining with a deep convolutional neural network , 2016, Knowl. Based Syst..

[20]  Liping Han,et al.  Distance Weighted Cosine Similarity Measure for Text Classification , 2013, IDEAL.

[21]  Bing Liu,et al.  Mining and summarizing customer reviews , 2004, KDD.

[22]  B. K. Tripathy,et al.  Investigation of recurrent neural networks in the field of sentiment analysis , 2017, 2017 International Conference on Communication and Signal Processing (ICCSP).

[23]  El Habib Nfaoui,et al.  An Unsupervised Approach for Reputation Generation , 2019, Procedia Computer Science.

[24]  Andrea Esuli,et al.  Distributional Correspondence Indexing for Cross-Lingual and Cross-Domain Sentiment Classification (Extended Abstract) , 2018, IJCAI.

[25]  Li Zhao,et al.  Attention-based LSTM for Aspect-level Sentiment Classification , 2016, EMNLP.

[26]  Deepa Gupta,et al.  Fine Grained Sentiment Classification of Customer Reviews Using Computational Intelligent Technique , 2015 .

[27]  Khaled Shaalan,et al.  Ontological Optimization for Latent Semantic Indexing of Arabic Corpus , 2018, ACLING.

[28]  Yue Zhang,et al.  Learning Domain Representation for Multi-Domain Sentiment Classification , 2018, NAACL.

[29]  Claudio Moraga,et al.  The Influence of the Sigmoid Function Parameters on the Speed of Backpropagation Learning , 1995, IWANN.

[30]  Wonjoon Kim,et al.  Do product reviews really reduce search costs? , 2017, J. Organ. Comput. Electron. Commer..

[31]  Yang Yu,et al.  How Service-Related Factors Affect the Survival of B2T Providers: A Sentiment Analysis Approach , 2015, J. Organ. Comput. Electron. Commer..

[32]  Tanasanee Phienthrakul,et al.  Sentiment Classification Using Document Embeddings Trained with Cosine Similarity , 2019, ACL.