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The detection of products with manipulated reviews has received widespread research attention, given that a truthful, informative, and useful review helps to significantly lower the search effort and cost for potential customers. This study proposes a method to recognize products with manipulated online customer reviews by examining the sequence of each review’s sentiment, readability, and rating scores by product on randomness, considering the example of a Russian online retail site. Additionally, this study aims to examine the association between brand awareness and existing manipulation with products’ reviews. Therefore, we investigated the difference between well-known and unknown brands’ products online reviews with and without manipulated reviews based on the average star rating and the extremely positive sentiment scores. Consequently, machine learning techniques for predicting products are tested with manipulated reviews to determine a more useful one. It was found that about 20% of all product reviews are manipulated. Among the products with manipulated reviews, 44% are products of well-known brands, and 56% from unknown brands, with the highest prediction performance on deep neural network.

암호화폐 시장은 주식 시장과 같이 급격한 가격 변동, 과도한 투기열 등의 문제점을 가지고 있으며, 이러한 문제점들은 투자자들에게 큰 리스크로 작용하고, 안정적인 투자 전략의 수립을 어렵게 만든다. 본 연구의 주요 목적은 암호화폐 시장의 이러한 불안정성을 극복하기 위해 향상된 성능의 가격 등락 예측 모형을 구축하는 것이다. 특히, 딥러닝 기법인 CNN을 활용하여 기술적 지표와 감성 분석을 통한 감성 점수를 사용하는 것으로 예측 정확도를 높이는 것을 중점으로 한다. 2021년 9월부터 2023년 9월까지의 암호화폐 가격 시계열 데이터와 coindesk.com의 암호화폐 관련 뉴스를 주요 데이터로 활용하여 CNN 기법을 중심으로 한 예측 모형을 구축하며, 뉴스 기사의 감성 분석을 통해 추가적인 감성 점수를 도입하여 예측의 정확성을 높이고자 한다. 본 연구를 통해 구축된 예측 모형은 기존의 기술적 지표만을 사용한 예측 모형에 비해 높은 예측 정확도를 보일 것으로 기대되며, 투자자들에게 안정적인 투자 전략의 수립과 CNN을 이용한 이진분류 예측 모델의 발전에 기여할 것으로 예상된다.

The blooming of social media has simulated interest in sentiment analysis. Sentiment analysis aims to determine from a specific piece of content the overall attitude of its author in relation to a specific item, product, brand, or service. In sentiment analysis, the focus is on the subjective sentences. Hence, in order to discover and extract the subjective information from a given text, researchers have applied various methods in computational linguistics, natural language processing, and text analysis. The aim of this paper is to provide an in-depth up-to-date study of the sentiment analysis algorithms in order to familiarize with other works done in the subject. The paper focuses on the main tasks and applications of sentiment analysis. State-of-the-art algorithms, methodologies and techniques have been categorized and summarized to facilitate future research in this field.

This study develops an effective method for sentiment analysis of user-generated content on drug review websites, which has not been investigated extensively compared to other general domains, such as product reviews. A clause-level sentiment analysis algorithm is developed since each sentence can contain multiple clauses discussing multiple aspects of a drug. The method adopts a pure linguistic approach of computing the sentiment orientation (positive, negative, or neutral) of a clause from the prior sentiment scores assigned to words, taking into consideration the grammatical relations and semantic annotation (such as disorder terms) of words in the clause. Experiment results with 2,700 clauses show the effectiveness of the proposed approach, and it performed significantly better than the baseline approaches using a machine learning approach. Various challenging issues were identified and discussed through error analysis. The application of the proposed sentiment analysis approach will be useful not only for patients, but also for drug makers and clinicians to obtain valuable summaries of public opinion. Since sentiment analysis is domain specific, domain knowledge in drug reviews is incorporated into the sentiment analysis algorithm to provide more accurate analysis. In particular, MetaMap is used to map various health and medical terms (such as disease and drug names) to semantic types in the Unified Medical Language System (UMLS) Semantic Network.

Customer reviews for wireless earbuds were collected and preprocessed using Playwright and Requests-HTML libraries, ensuring high-quality and relevant data. This paper introduces sentiments associated with these aspects were identified using Recurrent Neural Networks (RNNs) and Bidirectional Encoder Representations from Transformers (BERT) enhanced with attention mechanisms, which helped focus on the most relevant text segments. The models were integrated using ensemble methods, specifically Voting+BERT and Bagging+BERT, to improve accuracy and robustness. The Bagging+BERT model achieved the best performance, with an accuracy of 89.9 %, outperforming traditional machine learning models like Bayesian and logistic regression by 9.6 % and 8.7 %, respectively.

Sentiment Analysis of Malayalam Tweets using Machine Learning techniques is done in this paper. The tweets are classified into positive and negative using different machine learning techniques such as Naive Bayes (NB), Support Vector Machine (SVM) and Random Forest (RF). The different features like Bag of Words (BOW), Term Frequency vs. Inverse Document Frequency (TF IDF), Unigram with Sentiwordnet, and Unigram with Sentiwordnet including negation words are considered for feature vector formation of input dataset. The Random Forest classifier shows higher accuracy while considering Unigram with Sentiwordnet including negation words as a feature.

All government policies have a down side and the burden of the down side is often felt only by the common man. This paper considers one such government policy, the demonetization of high denomination currency by the Indian government that took effect midnight on November 8, 2016. In this paper, we have minutely analyzed this government policy from the common person’s perspective by using the concept of sentiment analysis and taking Twitter as a tool. In addition to performing a nation-wide analysis, we have also performed state-wide analysis using geolocation to further elucidate the reasons of displeasure among people of respective states.

Using a rich resource language to classify sentiments in a language with few resources is a popular subject of research in natural language processing. Burmese is a low-resource language. In light of the scarcity of labeled training data for sentiment classification in Burmese, in this study, we propose a method of transfer learning for sentiment analysis of a language that uses the feature transfer technique on sentiments in English. This method generates a cross-language word-embedding representation of Burmese vocabulary to map Burmese text to the semantic space of English text. A model to classify sentiments in English is then pre-trained using a convolutional neural network and an attention mechanism, where the network shares the model for sentiment analysis of English. The parameters of the network layer are used to learn the cross-language features of the sentiments, which are then transferred to the model to classify sentiments in Burmese. Finally, the model was tuned using the labeled Burmese data. The results of the experiments show that the proposed method can significantly improve the classification of sentiments in Burmese compared to a model trained using only a Burmese corpus.

This study evaluates the viewpoints of user focus incidents using microblog sentiment analysis, which has been actively researched in academia. Most existing works have adopted traditional supervised machine learning methods to analyze emotions in microblogs; however, these approaches may not be suitable in Chinese due to linguistic differences. This paper proposes a new microblog sentiment analysis method that mines associated microblog emotions based on a popular microblog through user-building combined with spectral clustering to analyze microblog content. Experimental results for a public microblog benchmark corpus show that the proposed method can improve identification accuracy and save manually labeled time compared to existing methods.

Aspect-based sentiment analysis is to discover the sentiment polarity towards an aspect from user-generated natural language. So far, most of the methods only use the implicit position information of the aspect in the context, instead of directly utilizing the position relationship between the aspect and the sentiment terms. In fact, neighboring words of the aspect terms should be given more attention than other words in the context. This paper studies the influence of different position embedding methods on the sentimental polarities of given aspects, and proposes a position embedding interactive attention network based on a long short-term memory network. Firstly, it uses the position information of the context simultaneously in the input layer and the attention layer. Secondly, it mines the importance of different context words for the aspect with the interactive attention mechanism. Finally, it generates a valid representation of the aspect and the context for sentiment classification. The model which has been posed was evaluated on the datasets of the Semantic Evaluation 2014. Compared with other baseline models, the accuracy of our model increases by about 2% on the restaurant dataset and 1% on the laptop dataset.

Recently, effort to obtain various information from the vast amount of social network services (SNS) big data generated in daily life has expanded. SNS big data comprise sentences classified as unstructured data, which complicates data processing. As the amount of processing increases, a rapid processing technique is required to extract valuable information from SNS big data. We herein propose a system that can extract human sentiment information from vast amounts of SNS unstructured big data using the naïve Bayes algorithm and natural language processing (NLP). Furthermore, we analyze the effectiveness of the proposed method through various experiments. Based on sentiment accuracy analysis, experimental results showed that the machine learning method using the naïve Bayes algorithm afforded a 63.5% accuracy, which was lower than that yielded by the NLP method. However, based on data processing speed analysis, the machine learning method by the naïve Bayes algorithm demonstrated a processing performance that was approximately 5.4 times higher than that by the NLP method.

The study reported in this paper aimed to evaluate the topics and opinions of COVID-19 discussion found on Twitter. It performed topic modeling and sentiment analysis of tweets posted during the COVID-19 outbreak, and compared these results over space and time. In addition, by covering a more recent and a longer period of the pandemic timeline, several patterns not previously reported in the literature were revealed. Author-pooled Latent Dirichlet Allocation (LDA) was used to generate twenty topics that discuss different aspects related to the pandemic. Time-series analysis of the distribution of tweets over topics was performed to explore how the discussion on each topic changed over time, and the potential reasons behind the change. In addition, spatial analysis of topics was performed by comparing the percentage of tweets in each topic among top tweeting countries. Afterward, sentiment analysis of tweets was performed at both temporal and spatial levels. Our intention was to analyze how the sentiment differs between countries and in response to certain events. The performance of the topic model was assessed by being compared with other alternative topic modeling techniques. The topic coherence was measured for the different techniques while changing the number of topics. Results showed that the pooling by author before performing LDA significantly improved the produced topic models.

People nowadays use emoticons in their text increasingly in order to express their feelings or recapitulate their words. Earlier machine learning techniques only involve the classification of text, emoticons or images solely where emoticons with text have always been neglected, thus ignored lots of emotions. This research proposed an algorithm and method for sentiment analysis using both text and emoticon. In this work, both modes of data were analyzed in combined and separately with both machine learning and deep learning algorithms for finding sentiments from twitter based airline data using several features such as TF?IDF, Bag of words, N-gram, and emoticon lexicons. This research demonstrates that whenever emoticons are used, their associated sentiment dominates the sentiment conveyed by textual data analysis. Also, deep learning algorithms are found to be better than machine learning algorithms.

Sentiment analysis is one of the promising approaches for developing a point of interest (POI) recommendation system. It uses natural language processing techniques that deploy expert insights from user-generated content such as reviews and feedback. By applying sentiment polarities (positive, negative, or neutral) associated with each POI, the recommendation system can suggest the most suitable POIs for specific users. The proposed study combines two models for POI recommendation. The first model uses bidirectional long short-term memory (BiLSTM) to predict sentiments and is trained on an election dataset. It is observed that the proposed model outperforms existing models in terms of accuracy (99.52%), precision (99.53%), recall (99.51%), and F1-score (99.52%). Then, this model is used on the Foursquare dataset to predict the class labels. Following this, user and POI embeddings are generated. The next model recommends the top POIs and corresponding coordinates to the user using the LSTM model. Filtered user interest and locations are used to recommend POIs from the Foursquare dataset. The results of our proposed model for the POI recommendation system using sentiment analysis are compared to several state-of-the-art approaches and are found quite affirmative regarding recall (48.5%) and precision (85%). The proposed system can be used for trip advice, group recommendations, and interesting place recommendations to specific users.

Bullet-screen videos contain rich user-generated data. It is of great practical significance to utilize sentiment analysis technology and topic models to identify video topics by fusing multi-dimensional features. A novel video recommendation method (MSSA) based on multi-source sentiment analysis is proposed by fusing the sentiment features of bullet screens and the topic features of video subtitles. Firstly, the method performs sentiment analysis on the bullet screens and subtitles of videos, and constructs a user sentiment feature matrix and a video sentiment feature matrix. Secondly, the method clusters user groups with similar sentiment tendencies by extracting the temporal information of bullet screens posted by users. Next, the topic feature vectors of subtitle texts in video clips are calculated to obtain the topic similarity matrix among videos by fusing with the video label information. Afterwards, a sentiment-oriented video set is generated according to the differences in sentiment polarity between bullet screens and subtitles. Finally, online recommendation of bullet-screen videos is achieved by introducing a recommendation heat index. The MSSA method is validated on real-world datasets, and it conducts comparative experiments with other state-of-the-art methods to assess its recommendation coverage and accuracy. The experimental results show that the MSSA method can effectively enhance the performance of user sentiment clustering and the semantic alignment of video content topics, enabling it to effectively explore user interest characteristics, and optimize the quality of personalized video recommendation services.

In light of the hate crimes directed at the AAPI community during the coronavirus pandemic, a shooting in Atlanta, Georgia, in 2021 resulted in the deaths of six women of Asian descent. Following the shooting, a surge of tweets emerged with the hashtag #StopAsianHate, expressing a wide range of emotions and sentiments in response to both the shooting and the hate crimes that have occurred since the outbreak of COVID-19. The findings of the analysis reveal that negative sentiment consistently drives retweets on Twitter, underscoring the pivotal role of negative emotions in shaping tweet virality. However, positive emotions, particularly joy and fear, also demonstrate positive associations with retweets in specific instances, highlighting a nuanced relationship between emotional expressions and shareability. In summary, while negative emotions exert a significant impact, positive emotions-particularly joy and fear-also show positive correlations with retweet counts in certain contexts. This indicates that both negative and positive emotional expressions contribute to tweet shareability, although their effects vary depending on the specific emotion and situational context. Our findings provide significant insights into the literature on social media engagement, emphasizing the need for a nuanced understanding of user behavior and the intricate mechanisms governing online interactions. Future studies should investigate whether these findings are unique to AAPI members or applicable to the broader topic of racial hate crimes.

This study would understand the overall perception of our society about public libraries, analyzing the texts related to public libraries, utilizing the semantic connection network & sentiment analysis. For this purpose, this study collected data from the last five years with keywords, 'Library' and 'Lifelong Learning Center' from January 1, 2016 through November 30, 2020 through the blogs and cafés of major domestic portal sites. With the collected data, text mining, centrality of keywords, network structure, structural equipotentiality, and sensitivity analyses were conducted. As a result of the analysis, First, 'reading' and 'book' were identified as representative keywords that form the social perception of public libraries. Second, it turned out that there were keywords related to the use of the library and the untact service due to the recent spread of COVID-19. Third, in seeking a plan for the development of public libraries through the keywords drawn to have positive meanings, it is necessary to create continuous services that can form a new image of the library, breaking away from the existing fixed role and image of the library and increase the convenience of use. Fourth, facilities and facilities for library services were recognized from a neutral point of view. Fifth, the spread of infectious diseases, social distancing, and temporary closure and closure of libraries are negatively related to public libraries, and awareness of librarians has been identified as negative keywords.

Establishing impact of a research paper is essential for academia, industry and research community alike. The attempts made in this direction are hitherto limited to some objective metrics, largely based on the citation count. The number of citations has always been used as a measure for ascertaining quality and popularity of research papers. Though, citations play an essential role in academic research, sometimes researchers may cite a paper to just point out its weaknesses and infirmities. A subjective look into the sentiments behind citations of a research paper aids in understanding the opinion of the peer research community for a paper. Objective measures such as citing author’s impact factor and the publication’s impact factor, may help to quantify the weightage of citations themselves and should also be included in the assessment of impact of a research paper. In this paper, we formulate a model that combines both the objective and subjective metrics and forms basis for an index to objectively convey the impact of a research paper.

Online product reviews have been an important source for customers to make informed decisions when purchasing goods. Yet, it is nearly impossible for consumers to access all the available reviews online. Such problem could be overcome by employing a recommendation system. Collaborative filtering (CF) recommendation system recommends products based on users’ ratings which may not represent customers’ true opinions on the items they bought. In this study, ratings were substituted with those computed using the frequencies of positive and negative words and expressions obtained from product reviews when developing a sentiment-based recommendation system. The objective of this study is to compare three recommendation systems: traditional CF-based recommendation, sentiment-based recommendation utilizing publicly available lexicon, sentiment-based recommendation employing domain-specific words and expressions examined in the study. The experiments conducted using the data obtained from MakeupAlley.com indicated that sentiment-based recommendation system applying domain-specific words and expressions outperformed the other two systems.

The existing model for sentiment analysis of product reviews learned from past data and new data was labeled based on training. But new data was never used by the existing system for making a decision. The proposed Aspect-based multi-agent Deep Reinforcement learning Sentiment Analysis (ADRSA) model learned from its very first data without the help of any training dataset and labeled a sentence with aspect category and sentiment polarity. It keeps on learning from the new data and updates its knowledge for improving its intelligence. The decision of the proposed system changed over time based on the new data. So, the accuracy of the sentiment analysis using deep reinforcement learning was improved over supervised learning and unsupervised learning methods. Hence, the sentiments of premium customers on a particular site can be explored to other customers effectively. A dynamic environment with a strong knowledge base can help the system to remember the sentences and usage State Action Reward State Action (SARSA) algorithm with Bidirectional Encoder Representations from Transformers (BERT) model improved the performance of the proposed system in terms of accuracy when compared to the state of art methods.