Sentiment Analysis Concept

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sourceText	citation
	Liu, B. (2010). Sentiment Analysis and Subjectivity. Handbook of Natural Language Processing.
Sentiment Analysis, also called Opinion Mining, is one of the most recent research topics within the field of Information Processing. Textual information retrieval techniques are mainly focused on processing, searching or mining factual information. Facts have an objective component; however, there are other textual elements which express subjective characteristics. These elements are mainly opinions, sentiments, appraisals, attitudes, and emotions, which are the focus of Sentiment Analysis.	Serrano-Guerrero, J., Olivas, J.A., Romero, F.P., & Herrera-Viedma, E. (2015). Sentiment analysis: A review and comparative analysis of web services. Inf. Sci., 311, 18-38.
Sentiment analysis (SA) is a process of studying public opinion about an entity.	Sharma, D., Sabharwal, M., Goyal, V., & Vij, M. (2020). Sentiment Analysis Techniques for Social Media Data: A Review.
Sentiment analysis is the computational examination of end user’s opinion, attitudes and emotions towards a particular topic or product.	Singh, N., Tomar, D., & Sangaiah, A.K. (2020). Sentiment analysis: a review and comparative analysis over social media. Journal of Ambient Intelligence and Humanized Computing, 1-21.
Sentiment analysis, also called opinion mining, is the field of study that analyzes people’s opinions, sentiments, evaluations, appraisals, attitudes, and emotions towards entities such as products, services, organizations, individuals, issues, events, topics, and their attributes.	Liu, B. (2012). Sentiment Analysis and Opinion Mining. Synthesis Lectures on Human Language Technologies.
Sentiment analysis or opinion mining is the computational study of opinions, sentiments and emotions expressed in text.	Liu, B. (2010). Sentiment Analysis and Subjectivity. Handbook of Natural Language Processing.
	Cambria, E., Das, D., Bandyopadhyay, S., & Feraco, A. (2017). A Practical Guide to Sentiment Analysis.
Sentiments and opinions have also been used interchangeably, perhaps because most NLP research on opinions has focused on detecting their subjective part, which has been referred to as sentiment.	Kim, S., & Hovy, E. (2004). Determining the Sentiment of Opinions. COLING.
	Nandal, N., Tanwar, R., & Pruthi, J. (2020). Machine learning based aspect level sentiment analysis for Amazon products. Spatial Information Research, 1-7.
For decades, the research area was mostly ignored until massive amounts of opinions available on the Web gave birth to modern sentiment analysis	Mäntylä, M., Graziotin, D., & Kuutila, M. (2018). The evolution of sentiment analysis - A review of research topics, venues, and top cited papers. ArXiv, abs/1612.01556.
Since the year 2002, research in sentiment analysis has been very active.	Zhang, L., & Liu, B. (2017). Sentiment Analysis and Opinion Mining. Encyclopedia of Machine Learning and Data Mining.
	Tsytsarau, M., & Palpanas, T. (2011). Survey on mining subjective data on the web. Data Mining and Knowledge Discovery, 24, 478-514.
	Dave, K., Lawrence, S., & Pennock, D. (2003). Mining the peanut gallery: opinion extraction and semantic classification of product reviews. WWW '03.
The aim of sentiment analysis is to determine the attitudes of a writer or a speaker for a given topic.	Kaur, H., Mangat, V., & Nidhi (2017). A survey of sentiment analysis techniques. 2017 International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC), 921-925.
The aim of sentiment analysis is to determine the attitude of a speaker or a writer with respect to some topic or the overall contextual polarity of a document	Kidd, T., & Morris, L.R. (2017). Handbook of Research on Instructional Systems and Educational Technology. p.339
Generally speaking, sentiment analysis aims to determine the attitude of a writerwith respect to some topic or the overall contextual polarity of a document. Theattitude may be his or her judgment or evaluation, affective state (that is to say, theemotional state of the author when writing), or the intended emotional commu-nication (that is to say, the emotional effect the author wishes to have on thereader).
The attitude may be his or her judgment or evaluation, affective state i.e. the emotional state of the author when writing, or the intended emotional communication i.e. the emotional effect the author wishes to have on the reader
1) Affect is a predecessor to feelings and emotions. 2)Feelings are person-centered, conscious phenomena. 3)Emotions are preconscious social expressions of feelings and affect influenced by culture. 4)Sentiments are partly social constructs of emotions that develop over time and are enduring. 5) Opinions are personal interpretations of information that may or may not be emotionally charged.	Munezero, M., Montero, C., Sutinen, E., & Pajunen, J. (2014). Are They Different? Affect, Feeling, Emotion, Sentiment, and Opinion Detection in Text. IEEE Transactions on Affective Computing, 5, 101-111.
In the past years, sentiment analysis has been mainly considered as a classification problem in the setting of machine learning, e.g., polarity classification of sentiments to one of two categories, namely, positive and negative.	Liu, H., & Haig, E. (2017). Fuzzy information granulation towards interpretable sentiment analysis. Granular Computing, 2, 289-302.
In the early days, a simple classification according to the semantic polarity (positiveness, negativeness or neutralness) of a document was predominant, whereas in the meantime, research activities have shifted towards a more sophisticated modeling of sentiments.	Buechel, S., & Hahn, U. (2017). Readers vs. Writers vs. Texts: Coping with Different Perspectives of Text Understanding in Emotion Annotation. LAW@ACL.
Sentiment Analysis can be considered a classification process as illustrated in Fig. 1.	Medhat, W., Hassan, A., & Korashy, H. (2014). Sentiment analysis algorithms and applications: A survey. Ain Shams Engineering Journal, 5, 1093-1113.
, research activities have shifted towards a more sophisticated modeling of sentiments. This includes the extension from only few basic to more varied emotional classes sometimes even assigning real-valued scores (Strapparava and Mihalcea, 2007), the aggregation of multiple aspects of an opinion item into a composite opinion statement for the whole item (Schouten and Frasincar, 2016), and sentiment compositionality on sentence level (Socher et al., 2013).	Buechel, S., & Hahn, U. (2017). Readers vs. Writers vs. Texts: Coping with Different Perspectives of Text Understanding in Emotion Annotation. LAW@ACL.
Branching from the field of SA whose core intent is to analyze human language by extracting opinions, ideas, and thoughts through the assignment of polarities either negative, positive, or neutral is the subfield of emotion detection (ED), which seeks to extract finer-grained emotions such as happy, sad, angry, and so on, from human languages rather than coarse-grained and general polarity assignments in SA.	Acheampong, F.A., Wenyu, C., & Nunoo-Mensah, H. (2020). Text‐based emotion detection: Advances, challenges, and opportunities.
	Wang, Y., Luo, J., Niemi, R., Li, Y., & Hu, T. (2016). Catching Fire via "Likes": Inferring Topic Preferences of Trump Followers on Twitter. ICWSM.
	Wolny, W. (2016). Emotion Analysis of Twitter Data That Use Emoticons and Emoji Ideograms. ISD.
opinion mining is possible on four different levels, namely document level, sentence level, aspect level, and concept level. Document level (Moraes et al. 2013) of opinion mining is the most abstract level of sentiment analysis and so is not appropriate for precise evaluations. The result of this level of analysis is usually general information about the documents polarity which cannot be very accurate. Sentence level opinion mining (Marcheggiani et al. 2014) is a fine-grain analysis that could be more accurate. Since the polarity of the sentences of an opinion does not imply the same polarity for the whole of opinion necessarily, aspect level of opinion mining (Xia et al. 2015) have been considered by researchers as the third level of opinion mining and sentiment analysis. Concept level opinion mining is the forth level of sentiment analysis which focuses on the semantic analysis of the text and analyzes the concepts which do not explicitly express any emotion (Poria et al. 2014). Several recent surveys and reviews on sentiment analysis consider these levels of opinion mining from this point of view (Medhat et al. 2014; Ravi and Ravi 2015; Balazs and Velasquez 2016; Yan et al. 2017; Sun et al. 2017;Lo et al. 2017).	Hemmatian, F., & Sohrabi, M. (2017). A survey on classification techniques for opinion mining and sentiment analysis. Artificial Intelligence Review, 1-51.
In general, sentiment analysis has been investigated mainly at three levels [1]. In document level the main task is to classify whether a whole opinion document expresses a positive or negative sentiment. This level of analysis assumes that each document expresses opinions on a single entity. In sentence level the main task is to check whether each sentence expressed a positive, negative, or neutral opinion. This level of analysis is closely related to subjectivity classification, which distinguishes objective sentences that express factual information from subjective sentences that express subjective views and opinion.	Devika, M., Sunitha, C., & Ganesh, A. (2016). Sentiment Analysis: A Comparative Study on Different Approaches☆. Procedia Computer Science, 87, 44-49.
There is no fundamental difference between document and sentence level classifications because sentences are just short documents	Liu, B. (2012). Sentiment Analysis and Opinion Mining. Synthesis Lectures on Human Language Technologies.
There is no fundamental difference between document and sentence level classifications because sentences are just short documents	Liu, B. (2012). Sentiment Analysis and Opinion Mining. Synthesis Lectures on Human Language Technologies.
Document level and the sentence level analyses do not discover what exactly people liked and did not like. Aspect level performs finer-grained analysis. Instead of looking at language constructs (documents, paragraphs, sentences, clauses or phrases), aspect level directly looks at the opinion itself.	Devika, M., Sunitha, C., & Ganesh, A. (2016). Sentiment Analysis: A Comparative Study on Different Approaches☆. Procedia Computer Science, 87, 44-49.
	Ito, T., Tsubouchi, K., Sakaji, H., Yamashita, T., & Izumi, K. (2020). Word-Level Contextual Sentiment Analysis with Interpretability. AAAI.
There are many factors that make sentiment analysis difficult compared to traditional text classification. (1) Domain dependency, (2) Spam, (3) Limitation of classification filtering, (4) Asymmetry in availability of opinion mining software, (5) Incorporation of opinion with implicit and behavior data, (6) Natural language processing overheads.	Mukkarapu, C.S., & Vemula, R.K. (2014). Opinion Mining and Sentiment Analysis: A Survey.
Since opinion mining is a relatively new filed, thus there are several challenges to be faced. According to Reference [4] current techniques are just primitive for opinions and comparisons identification and extraction. Mainly these challenges are related to the authenticity of the extracted data and the methods used in it.	Seerat, B., & Azam, F. (2012). Opinion Mining: Issues and Challenges (A survey). International Journal of Computer Applications, 49, 42-51.
Besides the typical challenges known from natural language processing and text processing, many challenges for opinion mining in social media sources make the detection and processing of opinions a complicated task:(1)Noisy texts, (2)Language variations, (3) Relevance and boilerplate, (4) Target identification.	Petz, G., Karpowicz, M.A., Fürschuß, H., Auinger, A., Stríteský, V., & Holzinger, A. (2013). Opinion Mining on the Web 2.0 - Characteristics of User Generated Content and Their Impacts. CHI-KDD.
There are several challenges in the field of sentiment analysis. The most common challenges are given here. Firstly, Word Sense Disambiguation (WSD), a classical NLP problem is often encountered. Secondly, addressing the problem of sudden deviation from positive to negative polarity. Thirdly, negations, unless handled properly can completely mislead. Fourthly, keeping the target in focus can be a challenge [7].	Chandrakala, S., & Sindhu, C. (2012). Opinion Mining and Sentiment Classification: A Survey. SOCO 2012.
Although most works approach it as a simple categorization problem, sentiment analysis is actually a suitcase research problem that requires tackling many NLP tasks	Cambria, E., Poria, S., Gelbukh, A., & Thelwall, M. (2017). Sentiment Analysis Is a Big Suitcase. IEEE Intelligent Systems, 32, 74-80.
Such NLP problems are organized into three layers: syntactics, semantics, and pragmatics.

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