ConSent: Context-based sentiment analysis

Katz, Gilad; Ofek, Nir; Shapira, Bracha

doi:10.1016/j.knosys.2015.04.009

Cited by 69 publications

(47 citation statements)

References 65 publications

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“…Microblogging (twitter) and transcribed text is unstructured having more noise and therefore, lexicon-based techniques do not perform well (Katz et al 2015). Similarly depending upon the nature of platform structural information can also be incorporated e.g.…”

Section: Discussionmentioning

confidence: 99%

“…Machine learning techniques are more supportive to accommodate structural information e.g. meta-data as non-textual features (Katz et al 2015). ML techniques depend on the feature set to which proximity and context based features can also be added.…”

Section: Discussionmentioning

confidence: 99%

“…Introduction of Web2.0 in the last decade has enabled people to post their thoughts and opinions on a range of topics. The data produced online is growing all the time produced by people from different backgrounds (Katz et al 2015). Opinion mining makes use of this data generated by millions of users all over the world.…”

Section: Data Sourcesmentioning

confidence: 99%

“…Lexicons are independent of the source data and therefore it does not fall for over-fitting. But context not addressed either in this approach (Katz et al 2015;. Search engines are used to find the meaning of unknown opinion lexicons.…”

Section: Dictionary (Lexicon) Based Techniquesmentioning

confidence: 99%

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Sentiment analysis and the complex natural language

Khan

Durrani

Ali

et al. 2016

Complex Adapt Syst Model

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There is huge amount of content produced online by amateur authors, covering a large variety of topics. Sentiment analysis (SA) extracts and aggregates users’ sentiments towards a target entity. Machine learning (ML) techniques are frequently used as the natural language data is in abundance and has definite patterns. ML techniques adapt to domain specific solution at high accuracy depending upon the feature set used. The lexicon-based techniques, using external dictionary, are independent of data to prevent overfitting but they miss context too in specialized domains. Corpus-based statistical techniques require large data to stabilize. Complex network based techniques are highly resourceful, preserving order, proximity, context and relationships. Recent applications developed incorporate the platform specific structural information i.e. meta-data. New sub-domains are introduced as influence analysis, bias analysis, and data leakage analysis. The nature of data is also evolving where transcribed customer-agent phone conversation are also used for sentiment analysis. This paper reviews sentiment analysis techniques and highlight the need to address natural language processing (NLP) specific open challenges. Without resolving the complex NLP challenges, ML techniques cannot make considerable advancements. The open issues and challenges in the area are discussed, stressing on the need of standard datasets and evaluation methodology. It also emphasized on the need of better language models that could capture context and proximity.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Data Sourcesmentioning

confidence: 99%

Section: Dictionary (Lexicon) Based Techniquesmentioning

confidence: 99%

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Sentiment analysis and the complex natural language

Khan

Durrani

Ali

et al. 2016

Complex Adapt Syst Model

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“…There is big subjective data ever growing on various online sources that is produced by amateur authors (Katz et al 2015). It can be used for aspect extraction by analyzing the data at user, relationships and content levels (Tang et al 2014;Guellil and Boukhalfa 2015).…”

Section: Big Subjective Datamentioning

confidence: 99%

Lifelong aspect extraction from big data: knowledge engineering

Khan

Durrani

Khalid

et al. 2016

Complex Adapt Syst Model

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Traditional machine learning techniques follow a single shot learning approach. It includes all supervised, semi-supervised, transfer learning, hybrid and unsupervised techniques having a single target domain known prior to analysis. Learning from one task is not carried to the next task, therefore, they cannot scale up to big data having many unknown domains. Lifelong learning models are tailored for big data having a knowledge module that is maintained automatically. The knowledge-base grows with experience where knowledge from previous tasks helps in current task. This paper surveys topic models leading the discussion to knowledge-based topic models and lifelong learning models. The issues and challenges in learning knowledge, its abstraction, retention and transfer are elaborated. The state-of-the art models store word pairs as knowledge having positive or negative co-relations called must-links and cannot-links. The need for innovative ideas from other research fields is stressed to learn more varieties of knowledge to improve accuracy and reveal more semantic structures from within the data.

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