With accelerated evolution of the internet as websites, social networks, blogs, online portals, reviews, opinions, recommendations, ratings, and feedback are generated by writers. This writer generated sentiment content can be about books, people, hotels, products, research, events, etc. These sentiments become very beneficial for businesses, governments, and individuals. While this content is meant to be useful, a bulk of this writer generated content require using the text mining techniques and sentiment analysis. But there are several challenges facing the sentiment analysis and evaluation process. These challenges become obstacles in analyzing the accurate meaning of sentiments and detecting the suitable sentiment polarity. Sentiment analysis is the practice of applying natural language processing and text analysis techniques to identify and extract subjective information from text. This paper presents a survey on the sentiment analysis challenges relevant to their approaches and techniques.

A survey on sentiment analysis challenges

Sentiments or opinions from social media provide the most up-to-date and inclusive information, due to the proliferation of social media and the low barrier for posting the message. Despite the growing importance of sentiment analysis, this area lacks a concise and systematic arrangement of prior efforts. It is essential to: (1) analyze its progress over the years, (2) provide an overview of the main advances achieved so far, and (3) outline remaining limitations. Several essential aspects, therefore, are addressed within the scope of this survey. On the one hand, this paper focuses on presenting typical methods from three different perspectives (task-oriented, granularity-oriented, methodology-oriented) in the area of sentiment analysis. Specifically, a large quantity of techniques and methods are categorized and compared. On the other hand, different types of data and advanced tools for research are introduced, as well as their limitations. On the basis of these materials, the essential prospects lying ahead for sentiment analysis are identified and discussed.

A survey of sentiment analysis in social media

People are sharing their opinions, stories and reviews through online video sharing websites every day. Studying sentiment and subjectivity in these opinion videos is experiencing a growing attention from academia and industry. While sentiment analysis has been successful for text, it is an understudied research question for videos and multimedia content. The biggest setbacks for studies in this direction are lack of a proper dataset, methodology, baselines and statistical analysis of how information from different modality sources relate to each other. This paper introduces to the scientific community the first opinion-level annotated corpus of sentiment and subjectivity analysis in online videos called Multimodal Opinion-level Sentiment Intensity dataset (MOSI). The dataset is rigorously annotated with labels for subjectivity, sentiment intensity, per-frame and per-opinion annotated visual features, and per-milliseconds annotated audio features. Furthermore, we present baselines for future studies in this direction as well as a new multimodal fusion approach that jointly models spoken words and visual gestures.

MOSI: Multimodal Corpus of Sentiment Intensity and Subjectivity Analysis in Online Opinion Videos

Opinion Mining and Information Fusion

A huge amount of online information, rich web resources are highly unstructured and such natural language are not solvable by machine directly. The increased demand to capture opinions of general public about social events, campaigns and sales of the product has led to study of the field opinion mining and sentiment analysis. Opinion refers to extraction of lines in raw data which expresses an opinion. Sentiment analysis identifies polarity of extracted opinions. The major challenge lies in analyzing the sentiments and identifying emotions expressed in texts. This paper presents a survey which covers a problem of sentiment analysis, techniques and methods used for the same.

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A Survey of Opinion Mining and Sentiment Analysis

In this age, in this nation, public sentiment is everything. With it, nothing can fail; against it, nothing can succeed. Whoever molds public sentiment goes deeper than he who enacts statutes, or pronounces judicial decisions (Abraham Lincoln, 1858 ) [1]. It is apparent from President Lincoln's well known quote that legislators understood the force of open assumption quite a while prior. In today world, the Internet is the main source of information. An enormous amount of information and opinion online is scattered and unstructured with no machine to arrange it. Because of demand the public to know opinions about exact product and services, political issues, or social scientists. That’s led us to study of field Opining Mining and Sentiment Analysis. Opining Mining and Sentiment Analysis have recently played a significant role for researchers because analysis of online text is beneficial for the market research political issue, business intelligence, online shopping, and scientific survey from psychological. Sentiment Analysis identifies the polarity of extracted public opinions. This paper presents a survey which covers Opining Mining, Sentiment Analysis, techniques, tools and classification.

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Sentiment Analysis and Opinion Mining: A Survey

Sentiment Analysis is a continuous field of research in the content mining field. Sentiment Analysis is the computational treatment of feelings, opinions, and subjectivity of content. This study paper handles an extensive review of the last upgrade in this field. Numerous as of late proposed calculations' improvements and different Sentiment Analysis applications are examined and displayed quickly in this review. Sentiment Analysis has as of late assumed a huge part for specialists since examination of online content is helpful for the statistical surveying political issue, business insight, on the web shopping, and logical overview from mental The related fields to Sentiment Analysis that pulled in analysts as of late are talked about. The fundamental focus of this review is to give almost a full picture of Sentiment Analysis is methods and the related fields with brief points of interest. The principle commitments of this paper incorporate the modern classifications of countless articles and the delineation of the late pattern of research in Sentiment Analysis and its related area. General Terms Sentiment Analysis.

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Suad Alhojely

Papers

Sentiment Analysis and Opinion Mining: A Survey

Different Applications and Techniques for Sentiment Analysis