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Sentiment analysis is a cognitive tool to extract the emotional tone of a piece of text. It is an area of research that is actively pursued in Natural Language Processing. Social media, forums and blogs, among other places, have seen a massive surge in the number of personalized reviews since the emergence of Internet-based applications. Sentiment analysis is primarily concerned with the classification and prediction of users' thoughts and emotions from these reviews. In recent years, numerous deep learning techniques have emerged to achieve this task. This paper provides a technical summary of Sentiment Analysis using a Bidirectional LSTM network. This model is capable of handling long-term dependencies by introducing memory into the model for making better predictions. The Amazon Product Review dataset served as the source of data for this research. Concretely, the analysis is performed on 104,975 product reviews reflecting users' attitudes toward mobile electronics products. The proposed model attempts to classify the reviews into two categories: positive and negative. Finally, the paper outlines the results of the analysis and suggests potential avenues for future research. [ABSTRACT FROM AUTHOR]

In recent years, surprising amounts of news, messages, and reviews of products and services are generated in the online social media. Several efforts are being dedicated to detecting topics, as well as mining opinions in these unstructured texts. There are several approaches that compute opinion polarity, and some of them consider topics for their textual analysis. Nevertheless, discovering topics in opinions continues being challenging; due to opinions are generally short and informally written. Besides, opinions do not have a defined structure in several paragraphs; they are presented most of the time as a composition in a paragraph. In this paper, we propose a method to detect polarity in opinions by topics. Our proposal contributes to the fuzzy polarity calculation of detected topics in Spanish opinions. This method is comprised of three main steps: (1) preprocessing, (2) topic detection and (3) fuzzy polarity detection. It is important to notice the added values of this paper are: (1) the topic detection proposal based on the semantic processing when applying the clustering algorithm on opinion sentences, and (2) the evaluation of different aggregation operators for determining the opinion polarity from a fuzzy logic perspective. Aiming at assessing the quality of the resultant polarity detection by topics, we have conducted two main experiments over the Spanish corpus of opinions about Andalusian Hotels from the TripAdvisor site. The results have shown that our method is able to detect the topics correctly, as well as calculating their opinion polarities. [ABSTRACT FROM AUTHOR]

This paper introduces the investigation of users interests by topic modeling. This work proposes a methodology for end-to-end unsupervised users topics investigation by textual data. Using additive regularization of topic models with large topic counts, tokens frequency filtering, lemmatization and communities information, one found interpretable interests. This paper shows differences of extracted topics from users posts and communities ones, from public user posts and hidden information from subscriptions. These topics differ significantly. This paper proposes a topics evaluation method by testing model prediction on the semi-automated labeled dataset. To get a more stable topic model one suggests using an averaged topic distribution based on users subscriptions. For cases with a large number of topics, one proposes an auto labeling approach solving as a classification problem with a constant number of classes. [ABSTRACT FROM AUTHOR]

Abstract Sentiment analysis on social media such as Twitter has become a very important and challenging task. Due to the characteristics of such data—tweet length, spelling errors, abbreviations, and special characters—the sentiment analysis task in such an environment requires a non-traditional approach. Moreover, social media sentiment analysis is a fundamental problem with many interesting applications. Most current social media sentiment classification methods judge the sentiment polarity primarily according to textual content and neglect other information on these platforms. In this paper, we propose a neural network model that also incorporates user behavioral information within a given document (tweet). The neural network used in this paper is a Convolutional Neural Network (CNN). The system is evaluated on two datasets provided by the SemEval-2016 Workshop. The proposed model outperforms current baseline models (including Naive Bayes and Support Vector Machines), which shows that going beyond the content of a document (tweet) is beneficial in sentiment classification, because it provides the classifier with a deep understanding of the task. [ABSTRACT FROM AUTHOR]

The interest in social media sentiment analysis and opinion mining for public security events has increased over the years. The availability of social media platforms for communication provides a valuable source of information for sentiment analysis and opinion mining research. The content shared across the media gives potential input to the physical environment and social phenomena related to public security threats. The input has been used to: monitor public security threats or emergency events, analyzing sentiment and opinionated data for threat management and the detection of public security threat events using geographic location-based sentiment analysis. However, a systematic survey that describes the trends and latest developments in this domain is unavailable. This paper presents a survey of social media sentiment analysis and opinion mining for public security. This paper aims to: understand the progress of the current state-of-the-art, identify the research gaps, and propose potential future directions. In total, 200 articles published from 2016 to 2023 were considered in this survey. The taxonomy shows the key attributes and limitations of the work presented in the surveyed articles. Subsequently, the potential future direction of work on sentiment analysis in the public security domain is suggested for interested researchers. [ABSTRACT FROM AUTHOR]

Highlights • Novel method to assess social media users engagement during public global events. • Method considers three dimensions: location, time and sentiment analysis. • Unsupervised SA strategy based on semantic dependencies with propagation. • Novel language-agnostic network-oriented location approach without assumptions. • Coherent with disposition theory of individual users to players and group belonging. Abstract In recent years there has been intense work on the analysis of social media to support marketing campaigns. A proper methodology for sentiment analysis is a crucial asset in this regard. However, when monitoring major public events the behaviour or social media users may be strongly biased by punctual actions of the participating characters and the sense of group belonging, which is typically linked to specific geographical areas. In this paper, we present a solution combining a location prediction methodology with an unsupervised technique for sentiment analysis to assess automatically the engagement of social network users in different countries during an event with worldwide impact. As far as the authors know, this is the first time such techniques are jointly considered. We demonstrate that the technique is coherent with the intrinsic disposition of individual users to typical actions of the characters participating in the events, as well as with the sense of group belonging. [ABSTRACT FROM AUTHOR]

Abstract Sentiment Analysis (SA) focuses on the study and analysis of peoples' opinions, sentiments and emotions based on written language. It is currently a very active research area in NLP. The growth of Web 2.0 has given all internet users the additional power of "interactivity, interoperability, and collaboration [1]. This has rapidly opened the door for the development of social media and the large interaction between users in all walks of life. Social media platforms are currently exploited by many companies as a major channel to advertise and sell products. As such, tools are clearly needed to analyse peoples' opinions on and reviews of the various products, feedback on events, etc. In recent years, researchers on Arabic NLP have made some good effort tackling the problem of SA. These efforts have been more focused during the last couple of years on Arabic dialects and, to a lesser extent, on the dialects of the Maghreb region, even less on the Algerian Dialect (AlgD). The processing of this dialect is made even more complex with the frequent code switching by its speakers between Arabic and Latin letters. Facebook being widely used in the Arab world, and in Algeria more specifically, we are interested in this paper in the SA of Algerian users' comments on various Facebook pages. A painstaking pre-processing of a corpus of such comments is done, and two neural network models, MLP and CNN, are trained to classify comments as negative, neutral or positive. Though a complex dialect, we have obtained an 81.6% accuracy with the MLP network and 89.5% accuracy with the CNN. We find this as a very encouraging result. [ABSTRACT FROM AUTHOR]

As the prevalence of social media on the Internet, opinion mining has become an essential approach to analyzing so many data. Various applications appear in a wide range of industrial domains. Meanwhile, opinions have diverse expressions which bring along research challenges. Both of the practical demands and research challenges make opinion mining an active research area in recent years. In this paper, we present a review of Natural Language Processing (NLP) techniques for opinion mining. First, we introduce general NLP techniques which are required for text preprocessing. Second, we investigate the approaches of opinion mining for different levels and situations. Then we introduce comparative opinion mining and deep learning approaches for opinion mining. Opinion summarization and advanced topics are introduced later. Finally, we discuss some challenges and open problems related to opinion mining. [ABSTRACT FROM AUTHOR]

There has been an enormous increase in the volume of Arabic posts on many social networks, providing a rich source of opinions on a variety of topics. Opinion mining has attracted much research interest in the last decade, but has mostly been conducted using the English language. Opinion mining from Arabic social media is lagging behind, primarily due to challenges with processing the morphologically complex Arabic natural language and the lack of available tools and resources for extracting Arabic sentiments from text. This complex task is further exacerbated when dealing with Saudi dialects that do not abide by the formal grammatical structure of Modern Standard Arabic (MSA). Targeting the use-case of unemployment in Saudi Arabia, this paper investigates the major challenges facing sentiment analysis researchers of the informal Arabic language and documents how these challenges can be initially addressed by means of linguistic pre-processing of raw text and supervised machine learning (ML) to identify sentiment polarity. The initial experimental evaluation yielded satisfactory sentiment classification results while also demonstrating the benefit of using ML to identify the dominant features in deciding sentiment polarity, which can in turn be used in constructing a problem-domain knowledge base that can further improve the accuracy of sentiment classification. [ABSTRACT FROM AUTHOR]

With the explosion of Social media, Opinion mining has been used rapidly in recent years. However, a few studies focused on the precision rate of feature review’s and opinion word’s extraction. These studies do not come with any optimum mechanism of supplying required precision rate for effective opinion mining. Most of these studies are based on Naïve Bayes, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and classical ontology. These systems are still imperfect for classifying the feature reviews into more degrees of polarity terms (strong negative, negative, neutral, positive and strong positive). Further, the existing classical ontology-based systems cannot extract blurred information from reviews; thus, it provides poor results. In this regard, this paper proposes a robust classification technique for feature review’s identification and semantic knowledge for opinion mining based on SVM and Fuzzy Domain Ontology (FDO). The proposed system retrieves a collection of reviews about hotel and hotel features. The SVM identifies hotel feature reviews and filter out irrelevant reviews (noises) and the FDO is then used to compute the polarity term of each feature. The amalgamation of FDO and SVM significantly increases the precision rate of review’s and opinion word’s extraction and accuracy of opinion mining. The FDO and intelligent prototype are developed using Protégé OWL-2 (Ontology Web Language) tool and JAVA, respectively. The experimental result shows considerable performance improvement in feature review’s classification and opinion mining. [ABSTRACT FROM AUTHOR]

The goal of this research is to build a model to predict stock price movement using the sentiment from social media. Unlike previous approaches where the overall moods or sentiments are considered, the sentiments of the specific topics of the company are incorporated into the stock prediction model. Topics and related sentiments are automatically extracted from the texts in a message board by using our proposed method as well as existing topic models. In addition, this paper shows an evaluation of the effectiveness of the sentiment analysis in the stock prediction task via a large scale experiment. Comparing the accuracy average over 18 stocks in one year transaction, our method achieved 2.07% better performance than the model using historical prices only. Furthermore, when comparing the methods only for the stocks that are difficult to predict, our method achieved 9.83% better accuracy than historical price method, and 3.03% better than human sentiment method. [ABSTRACT FROM AUTHOR]

Sentiment Analysis is an area of study within Natural Language Processing that is concerned with identifying the mood or opinion of subjective elements within a text. This paper focuses on the various methods used for classifying a given piece of natural language text according to the opinions expressed in it i.e. whether the general attitude is negative or positive. We also discuss the two-step method (aspect classification followed by polarity classification) that we followed along with the experimental setup. [ABSTRACT FROM AUTHOR]