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Topic

Sentiment analysis

About: Sentiment analysis is a(n) research topic. Over the lifetime, 22176 publication(s) have been published within this topic receiving 460826 citation(s). The topic is also known as: opinion mining.

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Papers
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Open accessBook
Bo Pang1, Lillian Lee2Institutions (2)
08 Jul 2008-
Abstract: An important part of our information-gathering behavior has always been to find out what other people think. With the growing availability and popularity of opinion-rich resources such as online review sites and personal blogs, new opportunities and challenges arise as people now can, and do, actively use information technologies to seek out and understand the opinions of others. The sudden eruption of activity in the area of opinion mining and sentiment analysis, which deals with the computational treatment of opinion, sentiment, and subjectivity in text, has thus occurred at least in part as a direct response to the surge of interest in new systems that deal directly with opinions as a first-class object. This survey covers techniques and approaches that promise to directly enable opinion-oriented information-seeking systems. Our focus is on methods that seek to address the new challenges raised by sentiment-aware applications, as compared to those that are already present in more traditional fact-based analysis. We include material on summarization of evaluative text and on broader issues regarding privacy, manipulation, and economic impact that the development of opinion-oriented information-access services gives rise to. To facilitate future work, a discussion of available resources, benchmark datasets, and evaluation campaigns is also provided.

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7,180 Citations


Open access
01 Jan 2002-
Abstract: We consider the problem of classifying documents not by topic, but by overall sentiment, e.g., determining whether a review is positive or negative. Using movie reviews as data, we flnd that standard machine learning techniques deflnitively outperform human-produced baselines. However, the three machine learning methods we employed (Naive Bayes, maximum entropy classiflcation, and support vector machines) do not perform as well on sentiment classiflcation as on traditional topic-based categorization. We conclude by examining factors that make the sentiment classiflcation problem more challenging.

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Topics: Sentiment analysis (65%), Naive Bayes classifier (57%), Support vector machine (52%) ...read more

6,980 Citations


Open accessProceedings ArticleDOI: 10.1145/1014052.1014073
Minqing Hu1, Bing Liu1Institutions (1)
22 Aug 2004-
Abstract: Merchants selling products on the Web often ask their customers to review the products that they have purchased and the associated services. As e-commerce is becoming more and more popular, the number of customer reviews that a product receives grows rapidly. For a popular product, the number of reviews can be in hundreds or even thousands. This makes it difficult for a potential customer to read them to make an informed decision on whether to purchase the product. It also makes it difficult for the manufacturer of the product to keep track and to manage customer opinions. For the manufacturer, there are additional difficulties because many merchant sites may sell the same product and the manufacturer normally produces many kinds of products. In this research, we aim to mine and to summarize all the customer reviews of a product. This summarization task is different from traditional text summarization because we only mine the features of the product on which the customers have expressed their opinions and whether the opinions are positive or negative. We do not summarize the reviews by selecting a subset or rewrite some of the original sentences from the reviews to capture the main points as in the classic text summarization. Our task is performed in three steps: (1) mining product features that have been commented on by customers; (2) identifying opinion sentences in each review and deciding whether each opinion sentence is positive or negative; (3) summarizing the results. This paper proposes several novel techniques to perform these tasks. Our experimental results using reviews of a number of products sold online demonstrate the effectiveness of the techniques.

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  • Figure 6: Infrequent feature extraction
    Figure 6: Infrequent feature extraction
  • Figure 7: Predicting the orientations of opinion sentences
    Figure 7: Predicting the orientations of opinion sentences
  • Table 1: Recall and precision at each step of feature generation
    Table 1: Recall and precision at each step of feature generation
  • Table 2: Recall and precision of FASTR
    Table 2: Recall and precision of FASTR
  • Table 3: Results of opinion sentence extraction and sentence orientation prediction
    Table 3: Results of opinion sentence extraction and sentence orientation prediction
  • + 2

6,565 Citations


Open accessProceedings ArticleDOI: 10.3115/1118693.1118704
06 Jul 2002-
Abstract: We consider the problem of classifying documents not by topic, but by overall sentiment, e.g., determining whether a review is positive or negative. Using movie reviews as data, we find that standard machine learning techniques definitively outperform human-produced baselines. However, the three machine learning methods we employed (Naive Bayes, maximum entropy classification, and support vector machines) do not perform as well on sentiment classification as on traditional topic-based categorization. We conclude by examining factors that make the sentiment classification problem more challenging.

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Topics: Sentiment analysis (67%), Multiclass classification (61%), Relevance vector machine (59%) ...read more

6,353 Citations


Open accessProceedings ArticleDOI: 10.18653/V1/N18-1202
Matthew E. Peters1, Mark Neumann1, Mohit Iyyer2, Matt Gardner1  +3 moreInstitutions (4)
15 Feb 2018-
Abstract: We introduce a new type of deep contextualized word representation that models both (1) complex characteristics of word use (e.g., syntax and semantics), and (2) how these uses vary across linguistic contexts (i.e., to model polysemy). Our word vectors are learned functions of the internal states of a deep bidirectional language model (biLM), which is pre-trained on a large text corpus. We show that these representations can be easily added to existing models and significantly improve the state of the art across six challenging NLP problems, including question answering, textual entailment and sentiment analysis. We also present an analysis showing that exposing the deep internals of the pre-trained network is crucial, allowing downstream models to mix different types of semi-supervision signals.

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Topics: Textual entailment (57%), Text corpus (53%), Syntax (53%) ...read more

6,141 Citations


Performance
Metrics
No. of papers in the topic in previous years
YearPapers
2022111
20213,075
20203,362
20192,879
20182,541
20172,253

Top Attributes

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Topic's top 5 most impactful authors

Erik Cambria

191 papers, 14.7K citations

Soujanya Poria

77 papers, 6.6K citations

Amir Hussain

49 papers, 2.9K citations

Pushpak Bhattacharyya

47 papers, 1.5K citations

Bing Liu

36 papers, 16.8K citations

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