Movie review mining and summarization

By Zhuang Li, Feng Jing, Xiaoyan Zhu. International Conference on Information and Knowledge Management, 2006

997 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/308b39ea5aaf6f9a9d182142786c1b21d991dfd4

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In Artificial Intelligence

Citations in the corpus

Lifelong-RL: Lifelong Relaxation Labeling for Separating Entities and Aspects in Opinion Targets (2016, background): “Although many target extraction methods exist (Hu and Liu, 2004; Zhuang et al., 2006; Ku et al., 2006; Wang and Wang, 2008; Wu et al., 2009; Lin and He, 2009; Zhang et al., 2010; Mei et al., 2007; Li et al., 2010; Brody and Elhadad, 2010; Wang et al., 2010; Mukherjee and Liu, 2012; Fang and Huang,…”
Neural Network-Based Abstract Generation for Opinions and Arguments (2016, background): “Topic : This House supports the death penalty.”
Building a Sentiment Summarizer for Local Service Reviews (2008, neutral): “1e.g., tripadvisor.com or yelp.com 2e.g., maps.google.com, local.yahoo.com or maps.live.com/localsearch”
Sentiment Analysis and Subjectivity (2010, background): “Sentiment classification is perhaps the most widely studied topic [3, 6, 8, 12, 13, 15, 16, 18, 27, 28, 33, 34, 35, 44, 45, 62, 64, 66, 67, 68, 70, 71, 73, 79, 80, 86, 92, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 111].”
Latent aspect rating analysis without aspect keyword supervision (2011, background): “However, the aspect information is not always available, and it may be di.cult to pre-de.ne appropriate aspects without a good knowledge about what aspects are actually commented on in the reviews.”
Extracting Opinion Targets in a Single and Cross-Domain Setting with Conditional Random Fields (2010, methodology): “vised algorithm by Zhuang et al. (2006) represents the state-of-the-art on the movies dataset we also employ in our experiments.”
The Stanford Typed Dependencies Representation (2008, background): “In other domains, Zhuang et al. (2006) uses the representation to extract opinions about features in reviews and Meena and Prabhakar (2007) uses it to improve the quality of sentence-level sentiment analysis.”
Sentiment analysis of blogs by combining lexical knowledge with text classification (2009, methodology): “These methods range from manual approaches of developing domain-dependent lexicons [5] to semi-automated approaches [12, 33, 13, 32], and even an almost fully automated approach [28].”
Latent aspect rating analysis on review text data: a rating regression approach (2010, background): “However, existing works on aspect-based summarization [10, 21, 18, 26] only aimed at aggregating all the reviews and representing major opinions on diﬀerent aspects for a given topic.”
Large Scale Sentiment Analysis on Twitter with Spark (2016, background): “A more recent approach [27], builds a sentiment and emoticon lexicon to support multidimensional sentiment analysis of Twitter data.”
Double Embeddings and CNN-based Sequence Labeling for Aspect Extraction (2018, methodology): “, 2017) and unsupervised approaches (Hu and Liu, 2004; Zhuang et al., 2006; Mei et al., 2007; Qiu et al., 2011; Yin et al., 2016; He et al., 2017).”
Rated aspect summarization of short comments (2009, background): “For example, Zhuang and others [20] focused on movie review mining and summarization.”
A survey on classification techniques for opinion mining and sentiment analysis (2017, background): “More researches in the review mining have been focused on the product reviews (Zhuang et al. 2006).”
MeanSum: A Neural Model for Unsupervised Multi-Document Abstractive Summarization (2018, positive): “Zhuang et al. (2006) incorporate external databases to construct simiarly structured, movie-specific review summaries.”
Structure-Aware Review Mining and Summarization (2010, neutral): “Zhuang et al. (2006) summarized movie reviews by extracting object feature keywords and opinion keywords.”
Aspect term extraction for sentiment analysis in large movie reviews using Gini Index feature selection method and SVM classifier (2016, background): “[24] recommended a multi-knowledge based method of review mining system for movie reviews.”
Explorations in Automatic Book Summarization (2007, methodology): “Second, after brieﬂy describing a summarization system that has been successfully used for the summarization of short documents, we show how techniques that take into account the length of the documents can be used to signiﬁcantly improve the performance of this sys-tem.”