Sentiment analysis from product reviews using SentiWordNet as lexical resource

Purpose Customer reviews are one important source that contains valuable information for quality evaluation of products or services. Review sentences contain sentiment words that show whether a user’s opinion is positive or negative. When review sentence has mix opinions, having sentiment words of both polarities, it is difficult to conclude whether it is positive or negative opinion. The purpose of this study is to improve the detection of polarity in such situation. Design methodology approach In this research, methods such as part-of-speech tagging, polarity analysis and rules selection are used to identify the polarity. A set of rules called contrast and conditional polarity rules (CCPR) has been created to improve the polarity detection in cases when there is mixture of sentiment words used in contrast and conditional type of review sentences. The experiment is conducted with data sets from three domains, i.e. restaurant, electronic and Tripadvisor. Findings The experimental result confirms that CCPR rules have higher baseline of the polarity aggression. In restaurant domain, CCPR rules (62.07%) have increased 13.79% compared with the Pol_Agg_MPQA baseline (48.28%) and 13.79% compared with Pol_Agg_Senti baseline (48.28%). In electronic domain, CCPR rule (79.17%) is higher by 12.50% compared with the Pol_Agg_MPQA baseline (66.67%) and 16.67% compared with Pol_Agg_Senti baseline (62.50%). Another one, CCPR rule (70.83%) is higher by 8.33% compared with the Pol_Agg_MPQA baseline (62.50%) and 12.50% compared with Pol_Agg_Senti baseline (58.33%). In conclusion, result of experiment shows promising outcome with improvement in detecting the positivity and negativity of indirect sentence, especially for the case of sentence with indirect polarity. Originality value To address the problem of mix opinions in terms of polarities, this paper presents a rule-based approach to improve the result of identifying positivity and negativity in sentence with indirect polarities.

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“…Sentiment word detection with aggregate calculation of sentence-level polarity by using SentiWordNet dictionary (Cernian et al, 2015).…”

Section: Evaluation Settingsmentioning

confidence: 99%

Contrast and conditional polarity rule (CCPR) for detection of positivity and negativity in opinionated sentence with indirect polarity

Gan

Krol

2021

IJWIS

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“…Sentiwordnet was built for supporting sentiment analysis. It has negative and positive score 3 . Each word has a score between 0 and 1 3 .…”

Section: Sentiment Analysismentioning

confidence: 99%

“…It has negative and positive score 3 . Each word has a score between 0 and 1 3 . A word score can be converted from -1 to 1 [16][17] [18] or -3 to +3 [9].…”

Section: Sentiment Analysismentioning

confidence: 99%

Rule-based Sentiment Degree Measurement of Opinion Mining of Community Participatory in the Government of Surabaya

Putra

Helen

Barakbah

2018

emitter

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Diskominfo Surabaya, as a government agency, received much community participatory for improvement of governmental services, with increasing number of 698, 2717, 4176 and 4298 participatory data respectively in 2011, 2012, 2013 and 2014. It is challenging for Diskominfo Surabaya to set a target by giving the response back within 24 hours. Due to task complexity to address the degree of participatory and to categorize the group of participatory, they faced difficulty to fulfill the target. In this research, we present a new system for measuring the sentiment degree of community participatory. We provide 5 functions in our system, which are: (1) Data Collection, (2) Data Preprocessing, (3) Text Mining, (4) Sentiment Analysis and (5) Validation. We propose our rule-based technique for the sentiment analysis of opinion mining with detection of 8 important parts, which are (1) Verb, (2) Adjective, (3) Preposition, (4) Noun, (5) Adverb, (6) Symbol, (7) Phrase, and (8) Complimentary. For applicability of our proposed system, we made a series of experiment with 410 data of community participatory in Twitter for Diskominfo Surabaya and compared with other sentiment classification algorithms which are SVM and Naive Bayes Classifier. Our system performed 77.32% rate of accuracy and outperformed to other comparing algorithms.

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“…However, deep learning-based techniques have gained a lot of popularity in recent years (Truong and Lauw, 2019). A significant amount of works have been done which includes the analysis of opinions about hotel reviews ( (Kasper and Vela, 2011;Shi and Li, 2011)), product reviews ( (Cernian et al, 2015;Wei and Gulla, 2010;Fang and Zhan, 2015)) etc. Initially, sentiment analysis has been carried out mostly using text (Badjatiya et al, 2017;Davidson et al, 2017;Fortuna et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

AMFB: Attention based multimodal Factorized Bilinear Pooling for multimodal Fake News Detection

Kumari

Ekbal

2021

Expert Systems with Applications

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Sentiment analysis from product reviews using SentiWordNet as lexical resource

Cited by 35 publications

References 3 publications

Contrast and conditional polarity rule (CCPR) for detection of positivity and negativity in opinionated sentence with indirect polarity

Contrast and conditional polarity rule (CCPR) for detection of positivity and negativity in opinionated sentence with indirect polarity

Rule-based Sentiment Degree Measurement of Opinion Mining of Community Participatory in the Government of Surabaya

AMFB: Attention based multimodal Factorized Bilinear Pooling for multimodal Fake News Detection

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