Automatic Sentiment Analysis Research Articles

Wasserstein based transfer network for cross-domain sentiment classification

Automatic sentiment analysis of social media texts is of great significance for identifying people’s opinions that can help people make better decisions. Annotating data is time consuming and laborious, and effective sentiment analysis on domains lacking of labeled data has become a problem. Cross-domain sentiment classification is a promising task, which leverages the source domain data with rich sentiment labels to analyze the sentiment polarity of the target domain lacking supervised information. Most of the existing researches usually explore algorithms that select common features manually to bridge different domains. In this paper, we propose a Wasserstein based Transfer Network (WTN) to share the domain-invariant information of source and target domains. We benefit from BERT to achieve rich knowledge and obtain deep level semantic information of text. The recurrent neural network with attention is used to capture features automatically, and Wasserstein distance is applied to estimate feature representations of source and target domains, which could help to capture significant domain-invariant features by adversarial training. Extensive experiments on Amazon datasets demonstrate that WTN outperforms other state-of-the-art methods significantly. Especially, the model behaves more stable across different domains.

A comparative assessment of sentiment analysis and star ratings for consumer reviews

Electronic word of mouth (eWOM) is prominent and abundant in consumer domains. Both consumers and product/service providers need help in understanding and navigating the resulting information spaces, which are vast and dynamic. The general tone or polarity of reviews, blogs or tweets provides such help. In this paper, we explore the viability of automatic sentiment analysis (SA) for assessing the polarity of a product or a service review. To do so, we examine the potential of the major approaches to sentiment analysis, along with star ratings, in capturing the true sentiment of a review. We further model contextual factors (specifically, product type and review length) as two moderators affecting SA accuracy. The results of our analysis of 900 reviews suggest that different tools representing the main approaches to SA display differing levels of accuracy, yet overall, SA is very effective in detecting the underlying tone of the analyzed content, and can be used as a complement or an alternative to star ratings. The results further reveal that contextual factors such as product type and review length, play a role in affecting the ability of a technique to reflect the true sentiment of a review.

Evaluation of Student Feedback Within a MOOC Using Sentiment Analysis and Target Groups

Many course designers trying to evaluate the experience of participants in a MOOC will find it difficult to track and analyse the online actions and interactions of students because there may be thousands of learners enrolled in courses that sometimes last only a few weeks. This study explores the use of automated sentiment analysis in assessing student experience in a beginner computer programming MOOC. A dataset of more than 25,000 online posts made by participants during the course was analysed and compared to student feedback. The results were further analysed by grouping participants according to their prior knowledge of the subject: beginner, experienced, and unknown. In this study, the average sentiment expressed through online posts reflected the feedback statements. Beginners, the target group for the MOOC, were more positive about the course than experienced participants, largely due to the extra assistance they received. Many experienced participants had expected to learn about topics that were beyond the scope of the MOOC. The results suggest that MOOC designers should consider using sentiment analysis to evaluate student feedback and inform MOOC design.

Emote-Controlled

In recent years, streaming platforms for video games have seen increasingly large interest, as so-called esports have developed into a lucrative branch of business. Like for other sports, watching esports has become a new kind of entertainment medium, which is possible due to platforms that allow gamers to live stream their gameplay, the most popular platform being Twitch.tv. On these platforms, users can comment on streams in real time and thereby express their opinion about the events in the stream. Due to the popularity of Twitch.tv, this can be a valuable source of feedback for streamers aiming to improve their reception in a gaming-oriented audience. In this work, we explore the possibility of deriving feedback for video streams on Twitch.tv by analyzing the sentiment of live text comments made by stream viewers in highly active channels. Automatic sentiment analysis on these comments is a challenging task, as one can compare the language used in Twitch.tv with that used by an audience in a stadium, shouting as loud as possible in sometimes nonorganized ways. This language is very different from common English, mixing Internet slang and gaming-related language with abbreviations, intentional and unintentional grammatical and orthographic mistakes, and emoji-like images called emotes . Classic lexicon-based sentiment analysis techniques therefore fail when applied to Twitch comments. To overcome the challenge posed by the nonstandard language, we propose two unsupervised lexicon-based approaches that make heavy use of the information encoded in emotes, as well as a weakly supervised neural network–based classifier trained on the lexicon-based outputs, which is supposed to help generalization to unknown words by use of domain-specific word embeddings. To enable better understanding of Twitch.tv comments, we analyze a large dataset of comments, uncovering specific properties of their language, and provide a smaller set of comments labeled with sentiment information by crowdsourcing. We present two case studies showing the effectiveness of our methods in generating sentiment trajectories for events live streamed on Twitch.tv that correlate well with specific topics in the given stream. This allows for a new kind of implicit real-time feedback gathering for Twitch streamers and companies producing games or streaming content on Twitch. We make our datasets and code publicly available for further research. 1

An Intelligent High Performance Automatic Sentiment Analysis Model Creation using Deep Convolution Neural Network

With the advancement of data and communications technology, social media platforms and small news blogs serve as significant sources of data. In a small blogging forum, people can share their opinions, complaints, feelings and behaviors about the topic, current problems, and products. Emotional examination is an significant examination area in natural language processing that intends to target the emotion of the source material. Twitter is a well-liked stage where people around the globe can interrelate through user-produced messages. Data received from Twitter can give out as a primary source for many applications, together with event recognition, news recommendations as well as emergency supervision. In the categorization of emotions, recognition of suitable sub feature set acts an significant role. LIWC (Linguistic Inquiry and Word Count) is a research program for text examination to retrieve psychometric features from text documents. In this article this work present a psychometric method called the intelligent high performance automatic sentiment analysis model (IHPASAM) for Twitter emotion analysis. In this scheme, this work employed two main types of LIWC (linguistic processes along with psychological) as feature sets. To discover the predictive efficiency of dissimilar feature engineering systems, five supervised learning techniques (Naïve Bayes, logistic regression, k-nearest neighbor algorithm, support vector machines as well as convolution neural network) along with proposed Intelligent Deep Convolution Neural Network (IDCNN) are employed. Investigational outcome show that the ensemble feature sets provides a superior predictive efficiency than the individual set.

Automated sentiment analysis of Free-Comment: An indirect liking measurement?

Automated sentiment analysis of Free-Comment: An indirect liking measurement?

Accuracy of Automated Sentiment Analysis

The written word is the oldest and most common type of data. Today, mass literacy and cheap technology allow for greater word output per capita than ever before in human history. To keep pace, companies and scholars increasingly depend on automated analyses — not only of what people say (content) but also how they feel (sentiment). This makes it pertinent to understand the accuracy of these automated analyses. While information systems research has produced remarkable leaps of progress, the emphasis has been on innovation rather than evaluation. From an applied perspective, it is not clear whether leaderboard results for selected problems generalize across data sets and domains. In this article, we focus on sentiment analysis methods and assess performance across applications by combining a meta-analysis of 216 comparative computer science publications on 271 unique data sets with experimental evaluations of novel language models. To the best of our knowledge, this constitutes the most comprehensive assessment of sentiment analysis accuracy to date. We find that method choice explains only 10% of the variance in accuracy. Controlling for contextual factors such as data set and paper characteristics increases explanatory power to over 75%, suggesting differences across research problems matter. We find that accuracy of sentiment analysis can indeed approach 95% but can also fall below 50%. This shows that more nuanced benchmarks, rather than best attainable values for selected use cases, are more meaningful for an applied audience. We compute benchmark values that take both methodological choices and application context into account.

An Automated Sentiment Analysis of The Sun and USA Today's Anti-Trump Protests News Articles in 2018

An Automated Sentiment Analysis of The Sun and USA Today's Anti-Trump Protests News Articles in 2018

Validating Automated Sentiment Analysis of Online Cognitive Behavioral Therapy Patient Texts: An Exploratory Study.

IntroductionSentiment analysis may be a useful technique to derive a user’s emotional state from free text input, allowing for more empathic automated feedback in online cognitive behavioral therapy (iCBT) interventions for psychological disorders such as depression. As guided iCBT is considered more effective than unguided iCBT, such automated feedback may help close the gap between the two. The accuracy of automated sentiment analysis is domain dependent, and it is unclear how well the technology is applicable to iCBT. This paper presents an empirical study in which automated sentiment analysis by an algorithm for the Dutch language is validated against human judgment.MethodsA total of 493 iCBT user texts were evaluated on overall sentiment and the presence of five specific emotions by an algorithm, and by 52 psychology students who evaluated 75 randomly selected texts each, providing about eight human evaluations per text. Inter-rater agreement (IRR) between algorithm and humans, and humans among each other, was analyzed by calculating the intra-class correlation under a numerical interpretation of the data, and Cohen’s kappa, and Krippendorff’s alpha under a categorical interpretation.ResultsAll analyses indicated moderate agreement between the algorithm and average human judgment with respect to evaluating overall sentiment, and low agreement for the specific emotions. Somewhat surprisingly, the same was the case for the IRR among human judges, which means that the algorithm performed about as well as a randomly selected human judge. Thus, considering average human judgment as a benchmark for the applicability of automated sentiment analysis, the technique can be considered for practical application.Discussion/ConclusionThe low human-human agreement on the presence of emotions may be due to the nature of the texts, it may simply be difficult for humans to agree on the presence of the selected emotions, or perhaps trained therapists would have reached more consensus. Future research may focus on validating the algorithm against a more solid benchmark, on applying the algorithm in an application in which empathic feedback is provided, for example, by an embodied conversational agent, or on improving the algorithm for the iCBT domain with a bottom-up machine learning approach.

Visual-textual sentiment classification with bi-directional multi-level attention networks

Social network has become an inseparable part of our daily lives and thus the automatic sentiment analysis on social media content is of great significance to identify people’s viewpoints, attitudes, and emotions on the social websites. Most existing works have concentrated on the sentiment analysis of single modality such as image or text, which cannot handle the social media content with multiple modalities including both image and text. Although some works tried to conduct multi-modal sentiment analysis, the complicated correlations between the two modalities have not been fully explored. In this paper, we propose a novel Bi-Directional Multi-Level Attention (BDMLA) model to exploit the complementary and comprehensive information between the image modality and text modality for joint visual-textual sentiment classification. Specifically, to highlight the emotional regions and words in the image–text pair, visual attention network and semantic attention network are proposed respectively. The visual attention network makes region features of the image interact with multiple semantic levels of text (word, phrase, and sentence) to obtain the attended visual features. The semantic attention network makes semantic features of the text interact with multiple visual levels of image (global and local) to obtain the attended semantic features. Then, the attended visual and semantic features from the two attention networks are unified into a holistic framework to conduct visual-textual sentiment classification. Proof-of-concept experiments conducted on three real-world datasets verify the effectiveness of our model.

Estimating Determinants of Attrition in Eating Disorder Communities on Twitter: An Instrumental Variables Approach.

BackgroundThe use of social media as a key health information source has increased steadily among people affected by eating disorders (EDs). Research has examined characteristics of individuals engaging in online communities, whereas little is known about discontinuation of engagement and the phenomenon of participants dropping out of these communities.ObjectiveThis study aimed to investigate the characteristics of dropout behaviors among eating disordered individuals on Twitter and to estimate the causal effects of personal emotions and social networks on dropout behaviors.MethodsUsing a snowball sampling method, we collected a set of individuals who self-identified with EDs in their Twitter profile descriptions, as well as their tweets and social networks, leading to 241,243,043 tweets from 208,063 users. Individuals’ emotions are measured from their language use in tweets using an automatic sentiment analysis tool, and network centralities are measured from users’ following networks. Dropout statuses of users are observed in a follow-up period 1.5 years later (from February 11, 2016 to August 17, 2017). Linear and survival regression instrumental variables models are used to estimate the effects of emotions and network centrality on dropout behaviors. The average levels of attributes among an individual’s followees (ie, people who are followed by the individual) are used as instruments for the individual’s attributes.ResultsEating disordered users have relatively short periods of activity on Twitter with one half of our sample dropping out at 6 months after account creation. Active users show more negative emotions and higher network centralities than dropped-out users. Active users tend to connect to other active users, whereas dropped-out users tend to cluster together. Estimation results suggest that users’ emotions and network centralities have causal effects on their dropout behaviors on Twitter. More specifically, users with positive emotions are more likely to drop out and have shorter lasting periods of activity online than users with negative emotions, whereas central users in a social network have longer lasting participation than peripheral users. Findings on users’ tweeting interests further show that users who attempt to recover from EDs are more likely to drop out than those who promote EDs as a lifestyle choice.ConclusionsPresence in online communities is strongly determined by the individual’s emotions and social networks, suggesting that studies analyzing and trying to draw condition and population characteristics through online health communities are likely to be biased. Future research needs to examine in more detail the links between individual characteristics and participation patterns if better understanding of the entire population is to be achieved. At the same time, such attrition dynamics need to be acknowledged and controlled when designing online interventions so as to accurately capture their intended populations.

Automated Sentiment Analysis of Text Data with NLTK

At present, most of researches in natural language processing primarily are focused on deep learning way to solve accuracy problems. This paper discusses a branch of natural language processing, sentiment analysis. In order to implement the function of sentiment analysis efficiently, we built an algorithm by calling the NLTK library firstly. As a result, this method can roughly obtain the emotional scores of different sentences and compare them with the scores given by human being who create these sentences. The results are presented by correlation calculation and images such as boxplot. The analysis of the images demonstrates the deficiency of the method. In addition, for the results, we found the direction of learning to solve the accuracy problems and made reasonable arrangements and planning.

A Comprehensive Study on Lexicon Based Approaches for Sentiment Analysis

In recent years, it is seen that the opinion-based postings in social media are helping to reshape business and public sentiments, and emotions have an impact on our social and political systems. Opinions are central to mostly all human activities as they are the key influencers of our behaviour. Whenever we need to make a decision, we generally want to know others opinion. Every organization and business always wants to find customer or public opinion about their products and services. Thus, it is necessary to grab and study the opinions on the Web. However, finding and monitoring sites on the web and distilling the reviews remains a big task because each site typically contains a huge volume of opinion text and the average human reader will have difficulty in identifying the polarity of each review and summarizing the opinions in them. Hence, it needs the automated sentiment analysis to find the polarity score and classify the reviews as positive or negative. This article uses NLTK, Text blob and VADER Sentiment analysis tool to classify the movie reviews which are downloaded from the website www.rottentomatoes.com that is provided by the Cornell University, and makes a comparison on these tools to find the efficient one for sentiment classification. The experimental results of this work confirm that VADER outperforms the Text blob.

Quantitative Sentiment Analysis of Lyrics in Popular Music

Popular music has been changing significantly over the years, revealing clear, audible differences when compared with songs written in other eras. A pop music composition is normally made of two parts—the tune and the lyrics. Here we use a digital humanities and data science approach to examine how lyrics changed between the 1950’s and the more recent years, and apply quantitative analysis to measure these changes. To identify possible differences, we analyzed the sentiments expressed in the songs of the Billboard Hot 100, which reflects the preferences of popular music listeners and fans in each year. Automatic sentiment analysis of 6,150 Billboard 100 songs covering all the years from 1951 through 2016 shows clear and statistically significant changes in sentiments expressed through the lyrics of popular music, generally towards a more negative tone. The results show that anger, disgust, fear, sadness, and conscientiousness have increased significantly, while joy, confidence, and openness expressed in pop song lyrics have declined.

Sentiment analysis on Bahasa Indonesia tweets using Unibigram models and machine learning techniques

Sentiment analysis on English tweets has its challenges. In addition to frequent use of the informal language, the words used are usually less consistent, contain abbreviations, and mixed with local languages. In this study, we combined n-grams feature selection models, i.e., unigrams, bigrams, and unibigram (1 + 2-grams) to analyze the public opinion of the Bahasa Indonesia tweets about the presidential candidates of the Republic of Indonesia in the 2014 presidential election. The experiment was carried out using Naive Bayes classifiers, Maximum Entropy classifiers, and Support Vector Machines with and without stop words removal and stemming in pre-processed tweet documents. The experiment results show the best performance is achieved by Naïve Bayes classifiers with unibigram feature models without removing the stop words and stemming process. This method achieved high performance with the precision and recall up to 85.50% indicating that automatic sentiment analysis of tweet documents using well-known supervised learning methods is feasible for the Indonesian language. More interestingly is the fact that stop words removal and stemming process on the corpus made the classification performances worse compared with the corpus that experienced cleansing only.

Sentiment Analysis for Arabic Language: A Brief Survey of Approaches and Techniques

With the emergence of Web 2.0 technology and the expansion of on-line social networks, current Internet users have the ability to add their reviews, ratings and opinions on social media and on commercial and news web sites. Sentiment analysis aims to classify these reviews reviews in an automatic way. In the literature, there are numerous approaches proposed for automatic sentiment analysis for different language contexts. Each language has its own properties that makes the sentiment analysis more challenging. In this regard, this work presents a comprehensive survey of existing Arabic sentiment analysis studies, and covers the various approaches and techniques proposed in the literature. Moreover, we highlight the main difficulties and challenges of Arabic sentiment analysis, and the proposed techniques in literature to overcome these barriers.

Arabic Dialect Identification based on Probabilistic-Phonetic Modeling

The identification of Arabic dialects is considered to be the first pre-processing component for any natural language processing problem. This task is useful for automatic translation, information retrieval, opinion mining and sentiment analysis. In this purpose, we propose a statistical approach based on the phonetic modeling to identify the correspondent Arabic dialect for each input acoustic signal. The main idea consists first, and for each dialect, in calculating a referenced phonetic model. Second, for every input audio signal, we calculate an appropriate phonetic model. Third, we compare this latter to all referenced Arabic dialect models. Finally, we associate the input acoustic signal to the dialect where the referenced phonetic model minimizes the cosine similarity. The obtained results are satisfactory. Indeed, based on 117 audio sequences, we attain a classification rate of 93%. Supporting the achieved results and the coverage of most of Arabic dialects, this study can be a reference for future work addressing dialectical speech processing applications.

SentiStrength-SE: Exploiting domain specificity for improved sentiment analysis in software engineering text

Automated sentiment analysis in software engineering textual artifacts has long been suffering from inaccuracies in those few tools available for the purpose. We conduct an in-depth qualitative study to identify the difficulties responsible for such low accuracy. Majority of the exposed difficulties are then carefully addressed through building a domain dictionary and appropriate heuristics. These domain-specific techniques are then realized in SentiStrength-SE, a tool we have developed for improved sentiment analysis in text especially designed for application in the software engineering domain.Using a benchmark dataset consisting of 5,600 manually annotated JIRA issue comments, we carry out both qualitative and quantitative evaluations of our tool. We also separately evaluate the contributions of individual major components (i.e., domain dictionary and heuristics) of SentiStrength-SE. The empirical evaluations confirm that the domain specificity exploited in our SentiStrength-SE enables it to substantially outperform the existing domain-independent tools/toolkits (SentiStrength, NLTK, and Stanford NLP) in detecting sentiments in software engineering text.

Improving Sentiment Analysis of Short Informal Indonesian Product Reviews using Synonym Based Feature Expansion

Sentiment analysis in short informal texts like product reviews is more challenging. Short texts are sparse, noisy, and lack of context information. Traditional text classification methods may not be suitable for analyzing sentiment of short texts given all those difficulties. A common approach to overcome these problems is to enrich the original texts with additional semantics to make it appear like a large document of text. Then, traditional classification methods can be applied to it. In this study, we developed an automatic sentiment analysis system of short informal Indonesian texts using Naive Bayes and Synonym Based Feature Expansion. The system consists of three main stages, preprocessing and normalization, features expansion and classification. After preprocessing and normalization, we utilize Kateglo to find some synonyms of every words in original texts and append them. Finally, the text is classified using Naive Bayes. The experiment shows that the proposed method can improve the performance of sentiment analysis of short informal Indonesian product reviews. The best sentiment classification performance using proposed feature expansion is obtained by accuracy of 98%.The experiment also show that feature expansion will give higher improvement in small number of training data than in the large number of them.

RETRACTED ARTICLE: Classifying streaming of Twitter data based on sentiment analysis using hybridization

Twitter is a social media that developed rapidly in today’s modern world. As millions of Twitter messages are sent day by day, the value and importance of developing a new technique for detecting spammers become significant. Moreover, legitimate users are affected by means of spams in the form of unwanted URLs, irrelevant messages, etc. Another hot topic of research is sentiment analysis that is based on each tweet sent by the user and opinion mining of the customer reviews. Most commonly natural language processing is used for sentiment analysis. The text is collected from user’s tweets by opinion mining and automatic sentiment analysis that are oriented with ternary classifications, such as “positive,” “neutral,” and “negative.” Due to limited size, unstructured nature, misspells, slangs, and abbreviations, it is more challenging for researchers to find sentiments for Twitter data. In this paper, we collected 600 million public tweets using URL-based security tool and feature generation is applied for sentiment analysis. The ternary classification is processed based on preprocessing technique, and the results of tweets sent by the users are obtained. We use a hybridization technique using two optimization algorithms and one machine learning classifier, namely particle swarm optimization and genetic algorithm and decision tree for classification accuracy by sentiment analysis. The results are compared with previous works, and our proposed method shows a better analysis than that of other classifiers.