Opinion observer

Liu, Bing; Hu, Minqing; Cheng, Junsheng

doi:10.1145/1060745.1060797

Cited by 1,184 publications

(99 citation statements)

References 23 publications

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“…In particular the opinions expressed in user-generated content can provide powerful insights in many different contexts and the opinion mining literature is replete with a variety of significant challenges, mature techniques and significant results. This includes work in sub-areas such as sentiment analysis [31,44,8,62,63,64], aspect-oriented opinion mining [24,4,50,21,22,66,11], opinion summarization [21,32,70,58,26,45], opin-ion search and retrieval [67,68,40,23], and product review helpfulness estimation [69,28,42,33,15] to name but a few.…”

Section: Related Workmentioning

confidence: 99%

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Combining similarity and sentiment in opinion mining for product recommendation

et al. 2015

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In the world of recommender systems, so-called content-based methods are an important approach that rely on the availability of detailed product or item descriptions to drive the recommendation process. For example, recommendations can be generated for a target user by selecting unseen products that are similar to the products that the target user has liked or purchased in the past. To do this, content-based methods must be able to compute the similarity between pairs of products (unseen products and liked products, for example) and typically this is achieved by comparing product features or other descriptive elements. The approach works well when product descriptions are readily available and when they are detailed enough to afford an effective similarity comparison. But this is not always the case. Detailed product descriptions may not be available since they can be expensive to create and maintain. In this article we consider another source of product descriptions in the form of the user-generated reviews that frequently accompany products on the web. We ask whether it is possible to mine these reviews, unstructured and noisy as they are, to produce useful product descriptions that can be used in a recommendation system. In particular we describe a novel approach to product recommendation that harnesses not only the features that can be mined from user-generated reviews but also the expressions of sentiment that are associated with these features. We present a recommendation ranking strategy that combines similarity and sentiment to suggest products that are similar but superior to a query product according to the opinion of reviewers, and we demonstrate the practical benefits of this approach across a variety of Amazon product domains.

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Section: Related Workmentioning

confidence: 99%

“…For example, a technique to summarise and visualise opinions relating to product features mined from a set of customer reviews is presented in [21,32]. Further, work has been proposed to summarise contrasting viewpoints; see, for example [26,45].…”

Section: Related Workmentioning

confidence: 99%

Combining similarity and sentiment in opinion mining for product recommendation

et al. 2015

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“…Liu B [2,3] in his handbook provided basics of sentiment analysis and subjectivity (or opinion mining). Due to a wide variety of practical applications, it has been a very active research area in recent years.…”

Section: Literature Reviewmentioning

confidence: 99%

Improving the Accuracy for Sentence Level Sentiment Analysis

Jaitly¹

2018

ijarcs

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Sentiment Analysis is one of the trending topics in the Information and Technology field. In this paper, we tried to increase the efficiency of sentiment analysis. To achieve maximum accuracy, a sentence level analysis was performed by taking into account oxymoron (i.e. Figure of speech in which apparently contradictory terms appear in conjunction). We addressed the problem of sentiment analysis in twitter by classifying the tweets according to the sentiment expressed in them.

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“…From these audits the aspect are decide as a successive Noun term and thing phrases. We can get exact aspect by extricating persevering thing from the positive and negative reviews [3]. Hu and Liu proposed most exceptional approach for aspect distinguishing proof.…”

Section: Aspect Identification Of the Productmentioning

confidence: 99%

Review on Product Aspect Analysis and Ranking

2017

IJAERD

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Opinion observer

Cited by 1,184 publications

References 23 publications

Combining similarity and sentiment in opinion mining for product recommendation

Combining similarity and sentiment in opinion mining for product recommendation

Improving the Accuracy for Sentence Level Sentiment Analysis

Review on Product Aspect Analysis and Ranking

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