Product rating using sentiment analysis

Anto, Minara P; Antony, M. Anly; Muhsina, K M; Johny, Nivya; James, Vinay; Wilson, Aswathy

doi:10.1109/iceeot.2016.7755346

Cited by 32 publications

(8 citation statements)

References 3 publications

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“…Traditional research in this area used to incorporate extensive and effort intensive social polls and quits. But nowadays, new technology offers a brand-new and powerful novel way of doing so, together with the favor of general affection for social media, websites, forums, blogs, and microblogs, among other sources [3]. Sentiment Analysis has proven to be a remarkable instrument in different research areas and for diverse applications, including examples such as capturing public opinion and investor sentiment about finance issues, stock movements, products, or companies [4], [5].…”

Section: New Technologies Have Brought In the Very Last Few Years Newmentioning

confidence: 99%

On the Statistical and Temporal Dynamics of Sentiment Analysis

et al. 2020

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Despite the broad interest and use of sentiment analysis nowadays, most of the conclusions in current literature are driven by simple statistical representations of sentiment scores. On that basis, the generated sentiment evaluation consists nowadays of encoding and aggregating emotional information from a number of individuals and their populational trends. We hypothesized that the stochastic processes aimed to be measured by sentiment analysis systems will exhibit nontrivial statistical and temporal properties. We established an experimental setup consisting of analyzing the short text messages (tweets) of 6 user groups with different nature (universities, politics, musicians, communication media, technological companies, and financial companies), including in each group ten high-intensity users in their regular generation of traffic on social networks. Statistical descriptors were checked to converge at about 2000 messages for each user, for which messages from the last two weeks were compiled using a custom-made tool. The messages were subsequently processed for sentiment scoring in terms of different lexicons currently available and widely used. Not only the temporal dynamics of the resulting score time series per user was scrutinized, but also its statistical description as given by the score histogram, the temporal autocorrelation, the entropy, and the mutual information. Our results showed that the actual dynamic range of lexicons is in general moderate, and hence not much resolution is given within their end-of-scales. We found that seasonal patterns were more present in the time evolution of the number of tweets, but to a much lesser extent in the sentiment intensity. Additionally, we found that the presence of retweets added negligible effects over standard statistical modes, while it hindered informational and temporal patterns. The innovative Compounded Aggregated Positivity Index developed in this work proved to be characteristic for industries and at the same time an interesting way to identify singularities among peers. We conclude that temporal properties of messages provide with information about the sentiment dynamics, which is different in terms of lexicons and users, but commonalities can be exploited in this field using appropriate temporal digital processing tools.

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Section: New Technologies Have Brought In the Very Last Few Years Newmentioning

confidence: 99%

On the Statistical and Temporal Dynamics of Sentiment Analysis

et al. 2020

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“…After having the data processed, text mining is performed, for which SVM and Naive Bayes classification algorithms are implemented. These classifiers are very important, since they gave very encouraging results, leading the SVM that resulted in 98.7% accuracy, as shown in the results section, this percentage is very favourable since in some related works lower results are obtained as can be seen in the following researches [6], [10], and [20], these had results between 80% to 90% accuracy. The results shown in the graphs are made based on SVM and Naive Bayes algorithms, considering which of them obtained the highest percentages.…”

Section: Discussionmentioning

confidence: 79%

Sentiment Analysis Related of International Festival of Living Arts Loja-Ecuador Employing Knowledge Discovery in Text

Rivera-Guamán

Cumbicus-Pineda

López-Lapo

et al. 2021

Communications in Computer and Information Science

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Nowadays, the use of social networks is part of the daily life of most people, especially young people, to share content and opinions. Twitter is one of the most popular social networks, in which users are the first actors to generate a large amount of information. The analysis of Twitter data requires a systematic process of collection, processing and classification. The main objective of this work is to classify the data tweets in three different classes: Positive, Negative and Neutral Opinions, corresponding to the "International Festival of the Living Arts in Loja (FIAVL)" in the years between 2016 and 2019. The official account of the "FIAVL" produced a total of 18k tweets in Spanish language, which followed the different phases of the Knowledge Discovery in Text (KDT) methodology for its analysis and study. Vector Support Machines (SVM) and Naive Bayes (NB) were used to classify the classes, where an accuracy rate of 98.7% was obtained, with the neutral opinion prevailing over the rest of the classes with 57%, thus it could be concluded that there are no positive or negative opinions about the FIAVL.

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“…We relied on the helpfulness votes and mutual reinforcement between reviewers and products for those purposes. However, analyzing the review-text to predict the product rating and review helpfulness is an active area of research [ 39 , 40 , 41 , 42 , 43 , 44 , 45 ]. In the future, the predicted helpfulness of reviews suggested by these text-mining techniques may be compared and incorporated in the algorithm to determine the combined (including the thumb-up/down votes) helpfulness of the reviews, and consequently, the ratings of the products and the rank of the reviewers.…”

Section: Discussionmentioning

confidence: 99%

A new approach to expert reviewer detection and product rating derivation from online experiential product reviews

Sarkar

Ahmad

2021

Heliyon

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Consumer reviews have emerged as one of the most influential factors in a person's purchase behavior. The existing open-source approaches for detecting expert reviewers and determining product ratings suffer from limitations and are susceptible to manipulation. In this work, we addressed these limitations by developing two algorithms and evaluated them on three datasets from amazon.com (the largest dataset contains nearly eight million reviews). In the first algorithm, we used a combination of the existing open-source approaches such as filtering by volume of contribution, helpfulness ratio, volume of helpfulness, and deviation from the estimated actual rating to detect the experts. The second algorithm is based on link analytic mutual iterative reinforcement of product ratings and reviewers' weights. In the second algorithm, both reviewers and products carry weights reflecting their relative importance. The reviewers influence the product rating according to their weight. Similarly, the reviewers' weights are impacted by their amount of deviation from the estimated actual product rating and the product's weight. Our evaluation using three datasets from amazon.com found the second algorithm superior to the other algorithms in detecting experts and deriving product ratings, significantly reducing the avg. error and avg. Mean Squared Error of the experts over the best of the other algorithms even after maintaining similar product coverage and quantity of reviews.

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Product rating using sentiment analysis

Cited by 32 publications

References 3 publications

On the Statistical and Temporal Dynamics of Sentiment Analysis

On the Statistical and Temporal Dynamics of Sentiment Analysis

Sentiment Analysis Related of International Festival of Living Arts Loja-Ecuador Employing Knowledge Discovery in Text

A new approach to expert reviewer detection and product rating derivation from online experiential product reviews

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