Social Media Sentiment Analysis Research Articles

A Lexicon Weighted Sentiment Analysis Approach on Twitter

Sentiment analysis in social media has grabbed more considerable attention because the results of such studies are highly applicable in social, economic, and political contexts. This study aimed to present an approach for collecting data from Twitter while storing and analysing the data using the hadoop as a big data platform as well as a hybrid trial and error model using the Bayes theorem plus a dictionary of words for the sentiment analysis. This method classifies tweets in two positive and negative classes based on the probability of positive words and negative words. According to the results, the accuracy of the proposed approach boosted from 67% to 71%. Then a new idea was employed in form of a weighted dictionary to achieve a higher accuracy. As such. the accuracy of the proposed approach reached a rate of 78% according to the results of another analysis conducted on the same data.

A lexicon weighted sentiment analysis approach on Twitter

Sentiment analysis in social media has grabbed more considerable attention because the results of such studies are highly applicable in social, economic, and political contexts. This study aimed to present an approach for collecting data from Twitter while storing and analysing the data using the hadoop as a big data platform as well as a hybrid trial and error model using the Bayes theorem plus a dictionary of words for the sentiment analysis. This method classifies tweets in two positive and negative classes based on the probability of positive words and negative words. According to the results, the accuracy of the proposed approach boosted from 67% to 71%. Then a new idea was employed in form of a weighted dictionary to achieve a higher accuracy. As such. the accuracy of the proposed approach reached a rate of 78% according to the results of another analysis conducted on the same data.

Combining Textual Cues with Social Clues: Utilizing Social Features to Improve Sentiment Analysis in Social Media

ABSTRACTTraditional sentiment analysis methods do not perform well when applied to social media data. In this study, we propose an approach to improve sentiment analysis performance in the context of social media. Our approach utilizes three types of additional information that can be collected from social media platforms—personal preference, friend influence, and herding effect—to enrich the input features of a supervised sentiment classification model. We implement the approach on data sets collected from Twitter across two industries (airlines and wireless service providers) and present the performance improvement attained by combining social features with pure text‐based features. To further investigate the operational implications of this improvement, we develop a stylized service recovery model for customer relationship management in social media. Our work has implications for automating social media monitoring and, more broadly, for improving customer relationship management in organizations.

Making an Impact: An Initial Review of U.S. Sport League Corporate Social Responsibility Responses During COVID-19

COVID-19 has brought about an unprecedented time where a majority of major American sporting organizations have ceased competition. Corporate social responsibility (CSR) actions, historically an avenue for sport organizations to positively impact society, provide a compelling avenue of study during this time. While researchers have observed the role of CSR and crisis communication when the crisis arises from within the organization, there is a need to understand CSR shifts and responses when the crisis is on a societal level. This commentary examines efforts of major U.S. sport league CSR programs (National Basketball Association/Women's National Basketball Association, National Football League, Major League Baseball, Major League Soccer, and National Hockey League), starting in mid-March when the majority of organizations ceased competition. Data were gathered using a mixed-methods approach of qualitative interviews, secondary research, and social media sentiment analysis. Key findings included the emergence of two different approaches to CSR communication strategies among U.S. sport leagues as well as three clear themes of COVID-19-related communication: educate, assist, and inspire. In addition, this commentary provides an initial glance at consumer response to CSR programs, showing both positive and negative sentiment trends.

Sentiment analysis of social media posts on pharmacotherapy: A scoping review.

Social media is playing an increasingly central role in patient's decision‐making process. Advances in technology have enabled meaningful interpretation of discussions on social media. We conducted a scoping review to assess whether Sentiment Analysis (SA), a big data analytic tool, could be used to extract meaningful themes from social media discussions on pharmacotherapy. A keyword search strategy was used on the following databases: OneSearch, PubMed, Medline, EMBASE, and Cochrane. One hundred and ninety‐four titles were identified of which 10 studies were included. We extracted themes about uses and implications of SA of social media discussions on pharmacotherapy. Twitter was the most frequently analyzed platform. Assessment of public sentiment about a particular medication was the most common use of SA followed by detection of adverse drug reactions. Studies also revealed a significant impact of news media on public sentiment. Implications for real world practice include identifying reasons for a negative sentiment, detecting adverse drug reactions and using the impact of news media on social media sentiment to drive public health initiatives. The lack of a consistent approach to SA between the studies reflects the lack of a gold standard for the technology and consequently the need for future research. Sentiment Analysis is a promising technology that can allow us to better understand patient opinion regarding pharmacotherapy. This knowledge can be used to improve patient safety, patient‐ physician interaction, and also enhance the delivery of public health measures.

Deep Learning for Social Media Sentiment Analysis

Social media is used as a tool by many people to express their opinions. Sentiment analysis for social media is very important, as it allows information to be obtained about public opinion on government performance. The goal of this research is to learn about the opinions of Surabaya citizens, using deep learning methods. The data are extracted from the official Twitter accounts of the Surabaya government and a private radio station in Surabaya. The data are grouped into two categories: positive and negative sentiments. This research is conducted in three steps: data pre-processing, sentiment classification, and visualization. Data pre-processing is required before modelling approaches are applied. It is used to transform the unstructured text data into structured data. The data pre-processing consists of case folding, tokenizing, and the removal of stop words. Deep learning methods are then applied to the data. A Backpropagation Neural Network (BNN) and a Convolutional Neural Network (CNN) are used to perform the sentiment classification. The BNN and CNN are compared using various metrics, such as precision, sensitivity, and area under the receiver operating characteristic curve (AUC). A word cloud is then used to visualize the data and find the most frequent words in each class. The results show that the sentiment classification with CNN is better than that with the BNN because the values for the precision, sensitivity and AUC are higher.

HEMOS: A novel deep learning-based fine-grained humor detecting method for sentiment analysis of social media

In this paper we introduce HEMOS (Humor-EMOji-Slang-based) system for fine-grained sentiment classification for the Chinese language using deep learning approach. We investigate the importance of recognizing the influence of humor, pictograms and slang on the task of affective processing of the social media. In the first step, we collected 576 frequent Internet slang expressions as a slang lexicon; then, we converted 109 Weibo emojis into textual features creating a Chinese emoji lexicon. In the next step, by performing two polarity annotations with new “optimistic humorous type” and “pessimistic humorous type” added to standard “positive” and “negative” sentiment categories, we applied both lexicons to attention-based bi-directional long short-term memory recurrent neural network (AttBiLSTM) and tested its performance on undersized labeled data. Our experimental results show that the proposed method can significantly improve the state-of-the-art methods in predicting sentiment polarity on Weibo, the largest Chinese social network.

Public Perceptions of Veterinarians from Social and Online Media Listening.

The public perception of the veterinary medicine profession is of increasing concern given the mounting challenges facing the industry, ranging from student debt loads to mental health implications arising from compassion fatigue, euthanasia, and other challenging aspects of the profession. This analysis employs social media listening and analysis to discern top themes arising from social and online media posts referencing veterinarians. Social media sentiment analysis is also employed to aid in quantifying the search results, in terms of whether they are positivity/negativity associated. From September 2017-November 2019, over 1.4 million posts and 1.7 million mentions were analyzed; the top domain in the search results was Twitter (74%). The mean net sentiment associated with the search conducted over the time period studied was 52%. The top terms revealed in the searches conducted revolved mainly around care of or concern for pet animals. The recognition of challenges facing the veterinary medicine profession were notably absent, except for the mention of suicide risks. While undeniably influenced by the search terms selected, which were directed towards client–clinic related verbiage, a relative lack of knowledge regarding veterinarians’ roles in human health, food safety/security, and society generally outside of companion animal care was recognized. Future research aimed at determining the value of veterinarians’ contributions to society and, in particular, in the scope of One Health, may aid in forming future communication and education campaigns.

Social media sentiment analysis through parallel dilated convolutional neural network for smart city applications

Deep Learning is considered to leverage smart cities through social media sentiment analysis. The digital content in social media can be used for many smart city applications (SCAs)11Smart city applications.. Classical convolutional neural networks (CNNs) are challenging to parallelize and insufficient to capture long term contextual semantic features for sentiment analysis. In this perspective, this paper initially proposes a domain-specific distributed word representation (DS-DWR)22Domain Specific distributed word representation. with a considerably small corpus size induced from textual resources in social media. In DS-DWR, different Distributed Word Representations are concatenated to builds rich representations over the input sequence, which is worthwhile for infrequent and unseen terms. Second, a dilated convolutional neural network (D-CNN)33Dilated Convolutional Neural Network., which is composed of three parallel dilated convolutional neural network (PD-CNN)44Parallel dilated convolutional neural network. layers and a global average pooling (GAP)55Global Average Pooling. layer. Our considered parallel dilated convolution reduces dimension and incorporates an extension in the size of receptive fields without the loss of local information. Further, the long-term contextual semantic information is achieved by the use of different dilation rates. Experiments demonstrate that our architecture accomplishes comparable results with multiple hyperparameters tuning for better parallelism which leads to the minimized computational cost.

A Review on Implementation of Hashing based Sentiment Analysis of Social Media

A Review on Implementation of Hashing based Sentiment Analysis of Social Media

Entity-Sensitive Attention and Fusion Network for Entity-Level Multimodal Sentiment Classification

Entity-level (aka target-dependent) sentiment analysis of social media posts has recently attracted increasing attention, and its goal is to predict the sentiment orientations over individual target entities mentioned in users’ posts. Most existing approaches to this task primarily rely on the textual content, but fail to consider the other important data sources (e.g., images, videos, and user profiles), which can potentially enhance these text-based approaches. Motivated by the observation, we study entity-level multimodal sentiment classification in this article, and aim to explore the usefulness of images for entity-level sentiment detection in social media posts. Specifically, we propose an Entity-Sensitive Attention and Fusion Network (ESAFN) for this task. First, to capture the intra-modality dynamics, ESAFN leverages an effective attention mechanism to generate entity-sensitive textual representations, followed by aggregating them with a textual fusion layer. Next, ESAFN learns the entity-sensitive visual representation with an entity-oriented visual attention mechanism, followed by a gated mechanism to eliminate the noisy visual context. Moreover, to capture the inter-modality dynamics, ESAFN further fuses the textual and visual representations with a bilinear interaction layer. To evaluate the effectiveness of ESAFN, we manually annotate the sentiment orientation over each given entity based on two recently released multimodal NER datasets, and show that ESAFN can significantly outperform several highly competitive unimodal and multimodal methods.

Hybrid Approach for Sentiment Analysis of Twitter Posts Using a Dictionary-based Approach and Fuzzy Logic Methods

Recently, sentiment analysis of social media has become a hot topic because of the huge amount of information that is provided in these networks. Twitter is a popular social media application offers businesses and government the opportunities to share and acquire information. This article proposes a technique that aims at measuring customers' satisfaction with cloud service providers, based on their tweets. Existing techniques focused on classifying sentimental text as either positive or negative, while the proposed technique classifies the tweets into five categories to provide better information. A hybrid approach of dictionary-based and Fuzzy Inference Process (FIP) is developed for this purpose. This direction was selected for its advantages and flexibility in addressing complex problems, using terms that reflect on human behaviors and experiences. The proposed hybrid-based technique used fuzzy systems in order to accurately identify the sentiment of the input text while addressing the challenges that are facing sentiment analysis using various fuzzy parameters.

Survey on Sentiment Analysis in Social Media

Sentiment Analysis (SA) is a computational treatment of opinions and emotions. And it is the most active area of Natural Language Processing (NLP), Social Network Mining and Multimedia Data mining. Sentiment analysis can be done for decision making process like product reviews, stock marketing, political debates, news articles and election results prediction. The developing significance of sentiment analysis with the popularity of social network such as Facebook, Twitter, Instagram and Flicker. The users of the social media express their feelings with both text and visual content. Predicting emotion from textual is easy but with visual content is quite difficult so far. A visual content does not contain any objects, locations and actions but it has cues about emotions, sentiment. Sentiment analysis of huge scale visual content can help better extract user sentiment towards topics and events such as image tweets, GIFs, videos. In this paper we analyze various research based on role of sentiment analysis on social media and come out with the evaluation and efficiency of sentiment analysis.

Comparison of Naive Bayes and K-nearest neighbours for online transportation using sentiment analysis in social media

Nowadays, online transportation is one of the transportation that is increasingly preferred by people. It becomes important because people need transportation to be more effective and efficient. However, sentiment analysis is necessary to improve the quality of services on online transportation. Sentiment analysis includes the process of extracting opinions, sentiments, evaluations, and emotions of people about online transportation services on Twitter social media. To get more accuracy in classification, the opinion is taken in large amounts and classify into positive and negative class. There are several steps that use sentiment analysis. Data collection, pre-processing data, POS Tagging, and opinion classification use the Naive Bayes Classifier method, compared to the accuracy of the K-Nearest Neighbours method. The results of the comparison of Naive Bayes Classifier and K-Nearest Neighbours algorithms use 565 data tweets from Twitter, divided 500 trained data, and 65 test data. The result showed that the Naive Bayes Classifier algorithm had achieved the accuracy rate of 66.15%, and K-Nearest Neighbours algorithm produces the accuracy rate of 67.69%. From the results, the K-Nearest Neighbours algorithm perform better accuracy in sentiment classification than Naive Bayes Classifier.

Cryptocurrency – Sentiment Analysis in Social Media

Abstract The paper proposes the exploration, identification and development of a Java solution for extracting the sentiment related to the cryptocurrencies phenomenon, from the content of the posts of certain popular social networks. Detecting the positive, neutral or negative character of the sentiment is adopted as a relevant method of establishing the nature of the human perception on the topical issue defined by cryptocurrencies.

Optimization driven actor-critic neural network for sentiment analysis in social media

Purpose Sentiment analysis is the subfield of data mining, which is profusely used for studying the opinions of the users by analyzing their suggestions on the Web platform. It plays an important role in the daily decision-making process, and every decision has a great impact on daily life. Various techniques including machine learning algorithms have been proposed for sentiment analysis, but still, they are inefficient for extracting the sentiment features from the given text. Although the improvement in sentiment analysis approaches, there are several problems, which make the analysis inefficient and inaccurate. This paper aims to develop the sentiment analysis scheme on movie reviews by proposing a novel classifier. Design/methodology/approach For the analysis, the movie reviews are collected and subjected to pre-processing. From the pre-processed review, a total of nine sentiment related features are extracted and provided to the proposed exponential-salp swarm algorithm based actor-critic neural network (ESSA-ACNN) classifier for the sentiment classification. The ESSA algorithm is developed by integrating the exponentially weighted moving average (EWMA) and SSA for selecting the optimal weight of ACNN. Finally, the proposed classifier classifies the reviews into positive or negative class. Findings The performance of the ESSA-ACNN classifier is analyzed by considering the reviews present in the movie review database. From, the simulation results, it is evident that the proposed ESSA-ACNN classifier has improved performance than the existing works by having the performance of 0.7417, 0.8807 and 0.8119, for sensitivity, specificity and accuracy, respectively. Originality/value The proposed classifier can be applicable for real-world problems, such as business, political activities and so on.

Analyzing the Brazilian Financial Market through Portuguese Sentiment Analysis in Social Media

ABSTRACTAccording to the Efficient Market Hypothesis, financial market movements are dependent on news and external events that have a significant impact on the market value of companies. Thus, a great amount of applications has arisen to explore this knowledge through automatic sentiment and opinion extraction. The technique known as Sentiment Analysis (SA) aims to analyze opinions, sentiments, and emotions present in unstructured data, leading many papers to address the impact of news and social media publications on the financial market. However, the literature lacks works considering the effects of sentiment available on social media and its impacts on the Brazilian stock market. This work aims to conduct a study of the Brazilian stock market movement through SA in Twitter considering tree perspectives: (i) absolute number of tweet sentiments; (ii) tweets sentiments weighted by favorites; and (iii) tweets sentiments weighted by retweets. The analyzed period was the Brazilian electoral period of 2018 (01-Oct-2018 to 31-Dec-2018). In this paper, we first developed a comparison study with SA Machine Learning techniques (Naive Bayes, Support Vector Machines, Maximum Entropy, and Multilayer Perceptron) and then applied the best algorithm to establish the relations between sentiments and the Brazilian stock market movement considering different time frames (windows sizes). Results indicate that Multilayer Perceptron was the best technique to perform SA in Portuguese. In addition, we observed that the predominant sentiment in social media relates to the stock market movement, improving accuracy as long as windows sizes are increased.

It Is an Equal Failing to Trust Everybody and to Trust Nobody

Social media are providing a huge amount of information, in scales never possible before. Sentiment analysis is a powerful tool that uses social media information to predict various target domains (e.g., the stock market). However, social media information may or may not come from trustworthy users. To utilize this information, a very first critical problem to solve is to filter credible and trustworthy information from contaminated data, advertisements, or scams. We investigate different aspects of a social media user to score his/her trustworthiness and credibility. Furthermore, we provide suggestions on how to improve trustworthiness on social media by analyzing the contribution of each trust score. We apply trust scores to filter the tweets related to the stock market as an example target domain. While social media sentiment analysis has been on the rise over the past decade, our trust filters enhance conventional sentiment analysis methods and provide more accurate prediction of the target domain, here, the stock market. We argue that while it is a failing to ignore the information social media provide, effectively trusting nobody, it is an equal failing to trust everybody on social media too: Our filters seek to identify whom to trust.

Sentiment Analysis of Social Media on Childhood Vaccination: Development of an Ontology.

BackgroundAlthough vaccination rates are above the threshold for herd immunity in South Korea, a growing number of parents have expressed concerns about the safety of vaccines. It is important to understand these concerns so that we can maintain high vaccination rates.ObjectiveThe aim of this study was to develop a childhood vaccination ontology to serve as a framework for collecting and analyzing social data on childhood vaccination and to use this ontology for identifying concerns about and sentiments toward childhood vaccination from social data.MethodsThe domain and scope of the ontology were determined by developing competency questions. We checked if existing ontologies and conceptual frameworks related to vaccination can be reused for the childhood vaccination ontology. Terms were collected from clinical practice guidelines, research papers, and posts on social media platforms. Class concepts were extracted from these terms. A class hierarchy was developed using a top-down approach. The ontology was evaluated in terms of description logics, face and content validity, and coverage. In total, 40,359 Korean posts on childhood vaccination were collected from 27 social media channels between January and December 2015. Vaccination issues were identified and classified using the second-level class concepts of the ontology. The sentiments were classified in 3 ways: positive, negative or neutral. Posts were analyzed using frequency, trend, logistic regression, and association rules.ResultsOur childhood vaccination ontology comprised 9 superclasses with 137 subclasses and 431 synonyms for class, attribute, and value concepts. Parent’s health belief appeared in 53.21% (15,709/29,521) of posts and positive sentiments appeared in 64.08% (17,454/27,236) of posts. Trends in sentiments toward vaccination were affected by news about vaccinations. Posts with parents’ health belief, vaccination availability, and vaccination policy were associated with positive sentiments, whereas posts with experience of vaccine adverse events were associated with negative sentiments.ConclusionsThe childhood vaccination ontology developed in this study was useful for collecting and analyzing social data on childhood vaccination. We expect that practitioners and researchers in the field of childhood vaccination could use our ontology to identify concerns about and sentiments toward childhood vaccination from social data.

Social context in sentiment analysis: Formal definition, overview of current trends and framework for comparison

Sentiment analysis in social media is harder than in other types of text due to limitations such as abbreviations, jargon, and references to existing content or concepts. Nevertheless, social media provides more information beyond text, such as linked media, user reactions, and relations between users. We refer to this information as social context. Recent works have successfully leveraged the fusion of text with social context for sentiment analysis tasks. However, these works are usually limited to specific aspects of social context, and there have not been any attempts to analyze and apply social context systematically. This work aims to bridge this gap by providing three main contributions: 1) a formal definition of social context; 2) a framework for classifying and comparing approaches that use social context; 3) a review of existing works based on the defined framework.