Twitter sentiment analysis using hybrid Spider Monkey optimization method

Shekhawat, Sayar Singh; Shringi, Sakshi; Sharma, Harish

doi:10.1007/s12065-019-00334-2

Cited by 28 publications

(13 citation statements)

References 28 publications

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“…The obtained consequences are likened with the existing models like, a big data approach for topic arrangement and sentiment analysis of Twitter data (HL-NBC-SDA), 15 Twitter sentiment analysis with hybrid Spider Monkey optimization process (SMOK-SDA). 16 Moreover, the twitter data from the datasets such as SemEval, 12 product reviews, 13 and movie reviews 14 are obtained for evaluation. Moreover, twitter data from datasets such as SemEval, 15 product reviews, 12 and movie reviews 16 are obtained for evaluation.…”

Section: Resultsmentioning

confidence: 99%

“…16 Moreover, the twitter data from the datasets such as SemEval, 12 product reviews, 13 and movie reviews 14 are obtained for evaluation. Moreover, twitter data from datasets such as SemEval, 15 product reviews, 12 and movie reviews 16 are obtained for evaluation. Here 25,700 movie tweet dataset, 25,700 SemEval and 25,700 product reviews.…”

Section: Resultsmentioning

confidence: 99%

“…• Efficiency of proposed system is analyzed with existing methods, like HL-NBC-SDA, 15 SMOK-SDA. 16 The article is organized as below: Section 2 deliberates recent research works and discussions depending on sentimental analysis and OM from textual contents from online resources. Section 3 illustrates the complete work procedure with diagrammatic representation and pseudocodes.…”

Section: Introductionmentioning

confidence: 99%

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Development of argument based opinion mining model with sentimental data analysis from twitter content

Arumugam

2022

Concurrency and Computation

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In present scenario, social networks have developed massive in practice and society impact. Specifically, micro-blogging is on trend in various platforms, such as Twitter, Instagram to evaluate public opinions for various issues. In recent times, some methods are developed for evaluating Twitter messages, based on the sentiment and opinions presented in tweets, corresponding to the hash-tags and keywords. However, these models have some issues in handling the contradictory content and inconsistent data.Considering with this, this article presents an argument based opinion mining model with sentimental data analysis, for extracting specific argument, which is assessed in bottom-up manner from the content from society emotion's reflects on the messages. Moreover, this model makes the user to pull out the arguments from a document set, which contains content from commercial sites, to extract the mostly argued positive and negative content. This model use natural language processing techniques, extraction of argument words for defining the decisions. The classification Naive Bayes classification is used for categorizing the results widely under agreed or disagreed. The experimental results prove that the proposed model provides feasible and appropriate results in argument analysis from Twitter content.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Development of argument based opinion mining model with sentimental data analysis from twitter content

Arumugam

2022

Concurrency and Computation

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“…We then use the Naive Bayes classifier [ 96 , 97 ] to evaluate the positive and negative polarities of the comments. We use two environments, NLTK [ 98 ] and Spider [ 99 ] for sentiment analysis. Word charts in Fig 5 shows the wordcloud for positive and negative polarities of words found in the comments of the app.…”

Section: Methods: Ai-enabled Text Data Miningmentioning

confidence: 99%

ACCU3RATE: A mobile health application rating scale based on user reviews

et al. 2021

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Background Over the last decade, mobile health applications (mHealth App) have evolved exponentially to assess and support our health and well-being. Objective This paper presents an Artificial Intelligence (AI)-enabled mHealth app rating tool, called ACCU3RATE, which takes multidimensional measures such as user star rating, user review and features declared by the developer to generate the rating of an app. However, currently, there is very little conceptual understanding on how user reviews affect app rating from a multi-dimensional perspective. This study applies AI-based text mining technique to develop more comprehensive understanding of user feedback based on several important factors, determining the mHealth app ratings. Method Based on the literature, six variables were identified that influence the mHealth app rating scale. These factors are user star rating, user text review, user interface (UI) design, functionality, security and privacy, and clinical approval. Natural Language Toolkit package is used for interpreting text and to identify the App users’ sentiment. Additional considerations were accessibility, protection and privacy, UI design for people living with physical disability. Moreover, the details of clinical approval, if exists, were taken from the developer’s statement. Finally, we fused all the inputs using fuzzy logic to calculate the new app rating score. Results and conclusions ACCU3RATE concentrates on heart related Apps found in the play store and App gallery. The findings indicate the efficacy of the proposed method as opposed to the current device scale. This study has implications for both App developers and consumers who are using mHealth Apps to monitor and track their health. The performance evaluation shows that the proposed mHealth scale has shown excellent reliability as well as internal consistency of the scale, and high inter-rater reliability index. It has also been noticed that the fuzzy based rating scale, as in ACCU3RATE, matches more closely to the rating performed by experts.

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“…With yellow representing cluster 1, green is for cluster 2 and the most classified comments from K-Means are clustered in cluster 0 which are in purple. Our proposed application form of the algorithm is optimized and it is faster than several proposed algorithms in other different researches and examples such as [16], [22] and [23] which are implemented in different cases and datasets. So, the execution time of our proposed form of implementation on Albanian language is 1.662015799999999 seconds.…”

Section: A K-meansmentioning

confidence: 99%

Unsupervised Clustering of Comments Written in Albanian Language

Hoti¹,

Ajdari²

2021

IJACSA

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Now-a-days, social media and communications in social media have become very important for services providers and those play a key role in service quality improvement as well as in decision making. The services consumers' discussions usually are written in their local languages and extracting important knowledge sometimes is very hard and problematic. In this field the natural language processing techniques are helpful, but different languages have their specifics and difficulties, and some languages are not prosperous enough in the techniques and methods on NLP, especially the local speaking of the language. In this scientific paper, we have tried to solve such a problem for the Albanian language spoken in Kosovo. Namely, for a dataset of the comments, written in Albanian language in Kosovo (local speaking), collected from the social media, by use of unsupervised clustering techniques, to make clustering regarding the topic of discussion in the comment. In this research, the different techniques of text feature extraction (vectorization and others) and clustering algorithms (K-means, Spectral, Agglomerative, etc.), are used with the idea to find and define more appropriate techniques for the Albanian language. In this paper are shown the results of the conducted experiments as well as discussions about what to use in case of the Albanian language and other languages similar or in group with Albanian (those which have a weak NLP).

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Twitter sentiment analysis using hybrid Spider Monkey optimization method

Cited by 28 publications

References 28 publications

Development of argument based opinion mining model with sentimental data analysis from twitter content

Development of argument based opinion mining model with sentimental data analysis from twitter content

ACCU3RATE: A mobile health application rating scale based on user reviews

Unsupervised Clustering of Comments Written in Albanian Language

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