Approaches, Tools and Applications for Sentiment Analysis Implementation
17 Sep 2015-International Journal of Computer Applications (Foundation of Computer Science (FCS), NY, USA)-Vol. 125, Iss: 3, pp 26-33
TL;DR: The paper gives an overview of the different sentiment classification approaches and tools used for sentiment analysis and provides a classification of approaches with respect to features/techniques and advantages/limitations.
Abstract: The paper gives an overview of the different sentiment classification approaches and tools used for sentiment analysis. Starting from this overview the paper provides a classification of (i) approaches with respect to features/techniques and advantages/limitations and (ii) tools with respect to the different techniques used for sentiment analysis. Different application fields of application of sentiment analysis such as: business, politic, public actions and finance are also discussed in the paper.
Citations
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TL;DR: Despite the availability of numerous assessment tools, their overall reliability differs between readability (high) and understandability (low), so mixed strategies combining quantitative and qualitative evaluations would optimize assessment strategies.
137 citations
TL;DR: This work presents and assesses the power of various volumetric, sentiment, and social network approaches to predict crucial decisions from online social media platforms and suggests some future directions in respective election prediction using social media content.
Abstract: This work presents and assesses the power of various volumetric, sentiment, and social network approaches to predict crucial decisions from online social media platforms. The views of individuals play a vital role in the discovery of some critical decisions. Social media has become a well-known platform for voicing the feelings of the general population around the globe for almost decades. Sentiment analysis or opinion mining is a method that is used to mine the general population’s views or feelings. In this respect, the forecasting of election results is an application of sentiment analysis aimed at predicting the outcomes of an ongoing election by gauging the mood of the public through social media. This survey paper outlines the evaluation of sentiment analysis techniques and tries to edify the contribution of the researchers to predict election results through social media content. This paper also gives a review of studies that tried to infer the political stance of online users using social media platforms such as Facebook and Twitter. Besides, this paper highlights the research challenges associated with predicting election results and open issues related to sentiment analysis. Further, this paper also suggests some future directions in respective election prediction using social media content.
82 citations
21 Jul 2016
TL;DR: This work proposes a Text analysis framework for twitter data using Apache spark and hence is more flexible, fast and scalable and Naïve Bayes and Decision trees machine learning algorithms are used for sentiment analysis in the proposed framework.
Abstract: Today, we live in a ‘data age’. Due to rapid increase in the amount of user-generated data on social media platforms like Twitter, several opportunities and new open doors have been prompted for organizations that endeavour hard to keep a track on customer reviews and opinions about their products. Twitter is a huge fast emergent micro-blogging social networking platform for users to express their views about politics, products sports etc. These views are useful for businesses, government and individuals. Hence, tweets can be used as a valuable source for mining public's opinion. Sentiment analysis is a process of automatically identifying whether a user-generated text expresses positive, negative or neutral opinion about an entity (i.e. product, people, topic, event etc). The objective of this paper is to give step-by-step detail about the process of sentiment analysis on twitter data using machine learning. This paper also provides details of proposed approach for sentiment analysis. This work proposes a Text analysis framework for twitter data using Apache spark and hence is more flexible, fast and scalable. Naive Bayes and Decision trees machine learning algorithms are used for sentiment analysis in the proposed framework.
76 citations
10 Mar 2017
TL;DR: A detail survey of various machine learning techniques used in analyzing the sentiments and in opinion mining is presented and then compared with their accuracy, advantages and limitations of each technique.
Abstract: Sentimental Analysis is reference to the task of Natural Language Processing to determine whether a text contains subjective information and what information it expresses i.e., whether the attitude behind the text is positive, negative or neutral. This paper focuses on the several machine learning techniques which are used in analyzing the sentiments and in opinion mining. Sentimental analysis with the blend of machine learning could be useful in predicting the product reviews and consumer attitude towards to newly launched product. This paper presents a detail survey of various machine learning techniques and then compared with their accuracy, advantages and limitations of each technique. On comparing we get 85% of accuracy by using supervised machine learning technique which is higher than that of unsupervised learning techniques.
70 citations
TL;DR: It is concluded that the Lexicon-based approach outperforms Supervised Machine Learning approach not only in terms of Accuracy, Precision, Recall and F-measure but also in termsof economy of time and efforts used.
66 citations
References
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TL;DR: Two 10-item mood scales that comprise the Positive and Negative Affect Schedule (PANAS) are developed and are shown to be highly internally consistent, largely uncorrelated, and stable at appropriate levels over a 2-month time period.
Abstract: In recent studies of the structure of affect, positive and negative affect have consistently emerged as two dominant and relatively independent dimensions. A number of mood scales have been created to measure these factors; however, many existing measures are inadequate, showing low reliability or poor convergent or discriminant validity. To fill the need for reliable and valid Positive Affect and Negative Affect scales that are also brief and easy to administer, we developed two 10-item mood scales that comprise the Positive and Negative Affect Schedule (PANAS). The scales are shown to be highly internally consistent, largely uncorrelated, and stable at appropriate levels over a 2-month time period. Normative data and factorial and external evidence of convergent and discriminant validity for the scales are also presented.
34,482 citations
"Approaches, Tools and Applications ..." refers methods in this paper
...PANAS-t Eleven-sentiment psychometric scale Sentiment140 API that allows classifying tweets to polarity classes positive, negative and neutral....
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...Another tool is the PANAS-t [45]....
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...On considering the tools used for sentiments analysis, the most used tools for detecting the feelings polarity are Emoticons, LIWC, SentiStrengh, Senti WordNet, SenticNet, Happiness Index, AFINN, PANAS-t, Sentiment140, NRC, EWGA and FRN. Sentiment analysis is used mainly in different fields such as marketing, political and sociological....
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...On considering the tools used for sentiments analysis, the most used tools for detecting the feelings polarity (negative and positive affect) are discussed in the paper: Emoticons, LIWC, SentiStrengh, Senti WordNet, SenticNet, Happiness Index, AFINN, PANAS-t, Sentiment140, NRC, EWGA and FRN....
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...The PANAS-t tracks increases or decreases in sentiments over time; it is based on a large set of words associated with eleven moods: joviality, assurance, serenity, surprise, fear, sadness, guilt, hostility, shyness, fatigue, and attentiveness....
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TL;DR: WordNet1 provides a more effective combination of traditional lexicographic information and modern computing, and is an online lexical database designed for use under program control.
Abstract: Because meaningful sentences are composed of meaningful words, any system that hopes to process natural languages as people do must have information about words and their meanings. This information is traditionally provided through dictionaries, and machine-readable dictionaries are now widely available. But dictionary entries evolved for the convenience of human readers, not for machines. WordNet1 provides a more effective combination of traditional lexicographic information and modern computing. WordNet is an online lexical database designed for use under program control. English nouns, verbs, adjectives, and adverbs are organized into sets of synonyms, each representing a lexicalized concept. Semantic relations link the synonym sets [4].
15,068 citations
"Approaches, Tools and Applications ..." refers methods in this paper
...WordNet: a lexical database for English....
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...SentiWordNet is a lexical resource publicly available for supporting sentiment classification and opinion mining applications....
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...On considering the tools used for sentiments analysis, the most used tools for detecting the feelings polarity are Emoticons, LIWC, SentiStrengh, Senti WordNet, SenticNet, Happiness Index, AFINN, PANAS-t, Sentiment140, NRC, EWGA and FRN. Sentiment analysis is used mainly in different fields such as marketing, political and sociological....
[...]
...On considering the tools used for sentiments analysis, the most used tools for detecting the feelings polarity (negative and positive affect) are discussed in the paper: Emoticons, LIWC, SentiStrengh, Senti WordNet, SenticNet, Happiness Index, AFINN, PANAS-t, Sentiment140, NRC, EWGA and FRN....
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...Finally, in the dictionary based techniques, the idea is to first collect a small set of opinion words manually with known orientations, and then to grow this set by searching in the WordNet dictionary for their synonyms and antonyms....
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Book•
08 Jul 2008TL;DR: This survey covers techniques and approaches that promise to directly enable opinion-oriented information-seeking systems and focuses on methods that seek to address the new challenges raised by sentiment-aware applications, as compared to those that are already present in more traditional fact-based analysis.
Abstract: An important part of our information-gathering behavior has always been to find out what other people think. With the growing availability and popularity of opinion-rich resources such as online review sites and personal blogs, new opportunities and challenges arise as people now can, and do, actively use information technologies to seek out and understand the opinions of others. The sudden eruption of activity in the area of opinion mining and sentiment analysis, which deals with the computational treatment of opinion, sentiment, and subjectivity in text, has thus occurred at least in part as a direct response to the surge of interest in new systems that deal directly with opinions as a first-class object.
This survey covers techniques and approaches that promise to directly enable opinion-oriented information-seeking systems. Our focus is on methods that seek to address the new challenges raised by sentiment-aware applications, as compared to those that are already present in more traditional fact-based analysis. We include material on summarization of evaluative text and on broader issues regarding privacy, manipulation, and economic impact that the development of opinion-oriented information-access services gives rise to. To facilitate future work, a discussion of available resources, benchmark datasets, and evaluation campaigns is also provided.
7,452 citations
01 Jan 2002
TL;DR: In this paper, the problem of classifying documents not by topic, but by overall sentiment, e.g., determining whether a review is positive or negative, was considered and three machine learning methods (Naive Bayes, maximum entropy classiflcation, and support vector machines) were employed.
Abstract: We consider the problem of classifying documents not by topic, but by overall sentiment, e.g., determining whether a review is positive or negative. Using movie reviews as data, we flnd that standard machine learning techniques deflnitively outperform human-produced baselines. However, the three machine learning methods we employed (Naive Bayes, maximum entropy classiflcation, and support vector machines) do not perform as well on sentiment classiflcation as on traditional topic-based categorization. We conclude by examining factors that make the sentiment classiflcation problem more challenging.
6,980 citations
06 Jul 2002
TL;DR: This work considers the problem of classifying documents not by topic, but by overall sentiment, e.g., determining whether a review is positive or negative, and concludes by examining factors that make the sentiment classification problem more challenging.
Abstract: We consider the problem of classifying documents not by topic, but by overall sentiment, e.g., determining whether a review is positive or negative. Using movie reviews as data, we find that standard machine learning techniques definitively outperform human-produced baselines. However, the three machine learning methods we employed (Naive Bayes, maximum entropy classification, and support vector machines) do not perform as well on sentiment classification as on traditional topic-based categorization. We conclude by examining factors that make the sentiment classification problem more challenging.
6,626 citations
"Approaches, Tools and Applications ..." refers background or methods in this paper
...This method needs labeled data to train classifiers [6]....
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...The document sentiment classification approach is used by [6] that classify movie reviews by using supervised machine learning method....
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...The term sentiment analysis first appeared in [4], however the research on sentiments/opinions appeared earlier [5; 6; 7; 8; 9]....
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