Search

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]

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]

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]

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]