Recommendation System using Lexicon Based Sentimental Analysis with collaborative filtering

Pradhan, Rahul; Khandelwal, Vishal; Chaturvedi, Ankur; Sharma, Dilip Kumar

doi:10.1109/parc49193.2020.236571

Cited by 14 publications

(4 citation statements)

References 8 publications

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“…The system proposed has achieved greater f-measure efficiency than traditional systems. A recommendation based on the sentimental analysis was introduced by Pradhan et al [25]. They used sentimental analysis to suggest top k reviews based on positive and negative.…”

Section: Related Workmentioning

confidence: 99%

“…Statistical metrics are evaluated the system accuracy by using the numerical recommendation scores in the test dataset 1749 to compare the current user ratings for user-item pairs [10]. These metrics are mean absolute error and root mean squared error: Mean absolute error (MAE) is statistic metric used to calculate the average difference in all the absolute values between prediction and the actual rating [10], [25].…”

Section: Experimental Workmentioning

confidence: 99%

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Improving collaborative filtering using lexicon-based sentiment analysis

Sallam

Hussein

Mousa

2022

IJECE

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<span>Since data is available increasingly on the Internet, efforts are needed to develop and improve recommender systems to produce a list of possible favorite items. In this paper, we expand our work to enhance the accuracy of Arabic collaborative filtering by applying sentiment analysis to user reviews, we also addressed major problems of the current work by applying effective techniques to handle the scalability and sparsity problems. The proposed approach consists of two phases: the sentiment analysis and the recommendation phase. The sentiment analysis phase estimates sentiment scores using a special lexicon for the Arabic dataset. The item-based and singular value decomposition-based collaborative filtering are used in the second phase. Overall, our proposed approach improves the experiments’ results by reducing average of mean absolute and root mean squared errors using a large Arabic dataset consisting of 63,000 book reviews.</span>

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Section: Related Workmentioning

confidence: 99%

Section: Experimental Workmentioning

confidence: 99%

Improving collaborative filtering using lexicon-based sentiment analysis

Sallam

Hussein

Mousa

2022

IJECE

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“…Sentiment information can be extracted using various ways, including speaker recognition [23], physical activity recognition [24], philological signals [25], human facial features [26], and textual information expressed over social media. Sentiment analysis is employed in numerous fields for opinion mining, such as focusing on multi-level single and multi-word aspects to manifest several domains in Twitter datasets [27], in recommendation systems [28], being employed for business intelligence [29], for finding public opinion about a particular rule before presentation ("eRuleMaking") [30], in comments analysis [31], News Sentiment Analysis [32], movie reviews analysis [33], for analysing the sensitivity of particular content before publishing or advertising [34], or to determine public opinion before elections in different countries. Elections are a significant component for any democratic country which involves the expression of opinion using a vote.…”

Section: Related Workmentioning

confidence: 99%

Sentiment Analysis of before and after Elections: Twitter Data of U.S. Election 2020

et al. 2021

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U.S. President Joe Biden took his oath after being victorious in the controversial U.S. elections of 2020. The polls were conducted over postal ballot due to the coronavirus pandemic following delays of the announcement of the election’s results. Donald J. Trump claimed that there was potential rigging against him and refused to accept the results of the polls. The sentiment analysis captures the opinions of the masses over social media for global events. In this work, we analyzed Twitter sentiment to determine public views before, during, and after elections and compared them with actual election results. We also compared opinions from the 2016 election in which Donald J. Trump was victorious with the 2020 election. We created a dataset using tweets’ API, pre-processed the data, extracted the right features using TF-IDF, and applied the Naive Bayes Classifier to obtain public opinions. As a result, we identified outliers, analyzed controversial and swing states, and cross-validated election results against sentiments expressed over social media. The results reveal that the election outcomes coincide with the sentiment expressed on social media in most cases. The pre and post-election sentiment analysis results demonstrate the sentimental drift in outliers. Our sentiment classifier shows an accuracy of 94.58% and a precision of 93.19%.

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“…Behavioral analysis, also generally referred to as sentimental analysis, combines the usage of text processing and mining techniques to thoroughly evaluate the subjective mood of users about extracted contexts (Pradhan et. al., 2020;.…”

Section: Introductionmentioning

confidence: 99%

Self-Aware Contextual Behavior Analysis for Service Quality Assurance Over Social Networks

Deepanshi

Sinha

2021

Journal of Cases on Information Technology

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Social media allows people to share their ideologue through an efficient channel of communication. The social dialogues carry sentiment in expression regarding a particular social profile, trend, or topic. In our research, we have collected real-time user comments and feedbacks from Twitter portals of two food delivery services. This is followed by the extraction of the most prevalent contexts using natural language analytics. Further, our proposed algorithmic framework is used to generate a signed social network to analyze the product-centric behavioral sentiment. Analysis of sentiment with the fine-grained level about contexts gave a broader view to evaluate and perform contextual predictions. Customer behavior is analyzed, and the outcome is received in terms of positive and negative contexts. The results from our social behavioral model predicted the positive and negative contextual sentiments of customers, which can be further used to help in deciding future strategies and assuring service quality for better customer satisfaction.

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Recommendation System using Lexicon Based Sentimental Analysis with collaborative filtering

Cited by 14 publications

References 8 publications

Improving collaborative filtering using lexicon-based sentiment analysis

Improving collaborative filtering using lexicon-based sentiment analysis

Sentiment Analysis of before and after Elections: Twitter Data of U.S. Election 2020

Self-Aware Contextual Behavior Analysis for Service Quality Assurance Over Social Networks

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