We study document-level sentiment analysis in Chapter 3. The works in Diakopoulos and Shamma (Reference Diakopoulos and Shamma2010) and Sang and Bos (Reference Sang and Bos2012) used manually annotated sentiments of tweets for election prediction. Patients were directed to stay isolated from their loved ones, which harmed their mental health. In the realm of emotion detection, most researchers adopted Ekman and Plutchiks emotion model. Apart from the availability of a large amount of opinionated data in social media, opinions and sentiments have a very wide range of applications because opinions are central to almost all human activities. In Merriam-Websters dictionary, sentiment is defined as an attitude, thought, or judgment prompted by feeling, whereas opinion is defined as a view, judgment, or appraisal formed in the mind about a particular matter. Machine Learning-based Techniques Emotion detection or classification may require different types of machine learning models such as Nave Bayes, support vector machine, decision trees, etc. Knowl-Based Syst 197:105918, Lvheim H (2012) A new three-dimensional model for emotions and monoamine neurotransmitters. (2021) constructed a multilingual corpus called MEmoFC, which stands for Multilingual Emotional Football Corpus, consisting of football reports from English, Dutch and German Web sites and match statistics crawled from Goal.com. Sentiment analysis is commonly seen as a subarea of NLP. An early patent on text classification included sentiment, appropriateness, humor, and many other concepts as possible class labels (Elkan, 2001). 2018; Liu etal. If someone reading the opinion cares a lot about the service, she probably will not go to eat at the restaurant. It is thus almost impossible to write a book that covers the ideas in every published paper. Such opinions will enable relevant government decision makers to respond quickly to fast-changing social, economic, and political climates. Bandhakavi etal. For instance, consider the sentence this place is so beautiful and post-tokenization, it will become 'this,' "place," is, "so," beautiful. It is essential to normalize the text for achieving uniformity in data by converting the text into standard form, correcting the spelling of words, etc. Many related names and slightly different tasks for example, sentiment analysis, opinion mining, opinion analysis, opinion extraction, sentiment mining, subjectivity analysis, affect analysis, emotion analysis, and review mining are now all under the umbrella of sentiment analysis. It is a data-driven approach where sentiment words along with context can be accessed. The authors then compared their proposed models with other existing baseline models and different datasets. on the Manage Your Content and Devices page of your Amazon account. The most common datasets are SemEval, Stanford sentiment treebank (SST), international survey of emotional antecedents and reactions (ISEAR) in the field of sentiment and emotion analysis. 2017). To create this dictionary, the first small set of sentiment words, possibly with very short contexts like negations, is collected along with its polarity labels (Bernab-Moreno etal. J Ambient Intell Hum Comput 12:41174126, Sasidhar TT, Premjith B, Soman K (2020) Emotion detection in hinglish (hindi+ english) code-mixed social media text. CHAPTER 1 INTRODUCTION 1.1 Introduction: Now Days in the modern world social media has become popular. In situations where the dataset is vast, the deep learning approach performs better than machine learning. Tokenization is the process of breaking down either the whole document or paragraph or just one sentence into chunks of words called tokens (Nagarajan and Gandhi 2019). Springer, pp 423430, Shamantha RB, Shetty SM, Rai P (2019) Sentiment analysis using machine learning classifiers: evaluation of performance. Many studies have acquired data from social media sites such as Twitter, YouTube, and Facebook and had it labeled by language and psychology experts in the literature. (Reference Oatley and Johnson-Laird2010) computed a sentiment score based simply on counting positive and negative sentiment words; this score was shown to correlate well with presidential approval, political election polls, and consumer confidence surveys. A deployed general-purpose sentiment analysis system and some case studies were reported in Castellanos et al. 2021). We can produce a sentiment profile of each social media participant based on his or her topical interests and opinions about these interests expressed in the users posts because a persons topical interests and opinions reflect the nature and preferences of the person. For example, in management sciences, the main focus is the impact of consumer opinions on businesses and ways to exploit such opinions to enhance business practices. Many people worldwide are now using blogs, forums, and social media sites such as Twitter and Facebook to share their opinions with the rest of the globe. Basic steps to perform sentiment analysis and emotion detection. } Zhang etal. Jian etal. Since the purpose of sentiment analysis is to determine polarity and categorize opinionated texts as positive or negative, datasets class range involved in sentiment analysis is not restricted to just positive or negative; it can be agreed or disagreed, good or bad. They use blogs and other discussion forums to interact with students who share similar interests and to assess the quality of possible colleges and universities. Note that neutral opinion usually means no opinion. This level of analysis is closely related to subjectivity classification (Wiebe et al., Reference Wang, Pan, Dahlmeier and Xiao1999), which distinguishes sentences that express factual information (objective sentences) from sentences that express subjective views and opinions (subjective sentences). Alqaryouti etal. With the explosive growth of the web and social media in the past twenty years, we now have a constant flow of opinionated data recorded in digital forms. In the past, when an individual needed opinions, he or she asked friends and family. Ye etal. That is, every subproblem of NLP is also a subproblem of sentiment analysis, and vice versa. 2020a). On the one hand, this application need provided a strong motivation for research. Computer Big Data Sentiment Intelligent Analysis Using Maximum Likelihood and Deep Learning. B ac k gr ou n d Word embeddings are essentially representations of words in a vector space and are necessary for sentiment analysis. The table shows that datasets include mainly the tweets, reviews, feedbacks, stories, etc. 2014. 4. It has also popularized two major research areas namely, social network analysis and sentiment analysis. For instance, in the sentence Y have u been soooo late?, 'why' is misspelled as 'y,' 'you' is misspelled as 'u,' and 'soooo' is used to show more impact. Results . https://doi.org/10.1109/WI-IAT.2012.170. 2015). The other challenge is the expression of multiple emotions in a single sentence. Moreover, this sentence does not express whether the person is angry or worried. Chatterjee etal. Knowl Inf Syst 62(8):151, ArchanaRao PN, Baglodi K (2017) Role of sentiment analysis in education sector in the era of big data: a survey. This type of opinion is similar to the concept of attitude in social psychology. These inclusions significantly broaden the research area and make it more comprehensive. In the healthcare sector, online social media like Twitter have become essential sources of health-related information provided by healthcare professionals and citizens. J Pers Soc Psychol 39(6):1161, Sailunaz K, Alhajj R (2019) Emotion and sentiment analysis from twitter text. The results derived using the Drugs.com dataset revealed that both frameworks performed better than traditional deep learning techniques. One way. (2010) used a model based upon neural networks technology for categorizing sentiments which consisted of sentimental features, feature weight vectors, and prior knowledge base. It stopped working in two days. Sarcasm is not so common in consumer reviews about products and services but is common in political discussions, which make political opinions hard to deal with. Such monitoring is especially big in China, where social media have become the most popular channel for the general public to voice their opinions about government policies and to expose corruptions, sex scandals, and other wrongdoings of government officials. "corePageComponentGetUserInfoFromSharedSession": true, (2020) created the dataset of Hindi-English code mixed with three basic emotions: happy, sad, and angry, and observed CNN-BILSTM gave better performance compared to others. However, gathering data is not difficult, but manual labeling of the large dataset is quite time-consuming and less reliable (Balahur and Turchi 2014). Since the dictionary-based approach does not consider the context around the sentiment word, it leads to less efficiency. Some evidence for a theory. In sentiment analysis, polarity is the primary concern, whereas, in emotion detection, the emotional or psychological state or mood is detected. In other words, they do not tell what each opinion is about that is, the target of opinion. We briefly introduce them here. One may say that if we can classify a sentence to be positive, everything in the sentence can take the positive opinion. In English, the word 'emotion' came into existence in the seventeenth century, derived from the French word 'emotion, meaning a physical disturbance. We simply applied our Opinion Parser system to identify and combine positive and negative opinions about each movie and user intentions to watch it; no additional model or algorithm was used. This participatory web and communications revolution has transformed both our everyday lives and the society as a whole. (2019) evaluated the machine learning algorithms like Nave Bayes, SVM, and decision trees to identify emotions in text messages. Such information can be used in many applications for example, recommending products and services and determining which political candidates for whom a person should vote. N-grams features perform better than the BOW approach as they cover syntactic patterns, including critical information (Chaffar and Inkpen 2011). Lang Resour Eval 51(3):833855, Article It represents a large ifferent methods used for classifying a documents polarity. Ray and Chakrabarti (2020) combined the rule-based approach to extract aspects with a 7-layer deep learning CNN model to tag each aspect. Google Scholar, Abdi A, Shamsuddin SM, Hasan S, Piran J (2019) Deep learning-based sentiment classification of evaluative text based on multi-feature fusion. Thus, Li etal. Int J Comput Sci Inf Technol 2(2):123128, Shirsat VS, Jagdale RS, Deshmukh SN (2019) Sentence level sentiment identification and calculation from news articles using machine learning techniques. https://doi.org/10.1007/978-3-540-71496-5_53, Mladenovi M, Mitrovi J, Krstev C, Vitas D (2016) Hybrid sentiment analysis framework for a morphologically rich language. Sailunaz and Alhajj (2019) used Ekman models for annotating tweets. Mishne and Glance (Reference Mishne and Glance2006) showed that positive sentiment is a better predictor of movie success than simple buzz (keyword) count. However, by ignoring the intensity of emotions, these traditional lexicons become less informative and less adaptable. Acquiring and analyzing public and consumer opinions have long been a huge business for marketing, public relations, and political campaign firms. Lecture notes in computer science, vol 4425. Opinion spamming has become a major issue in social media. Procedia Comput Sci 143:426433, AlAjrawi S, Agrawal A, Mangal H, Putluri K, Reid B, Hanna G, Sarkar M (2021) Evaluating business yelps star ratings using sentiment analysis. Soc Netw Anal Min 11(1):111, Songbo T, Jin Z (2008) An empirical study of sentiment analysis for Chinese documents. Hostname: page-component-7ff947fb49-xwnqc The most common datasets are SemEval, Stanford sentiment treebank (SST), international survey of emotional antecedents and reactions (ISEAR) in the field of sentiment and emotion analysis. Many sentences without sentiment words can imply positive or negative sentiments or opinions of their authors. In: Proceedings of the 2019 3rd international conference on computing methodologies and communication (ICCMC), IEEE, pp 11971200, Viegas F, Alvim MS, Canuto S, Rosa T, Gonalves MA, Rocha L (2020) Exploiting semantic relationships for unsupervised expansion of sentiment lexicons. Emot Rev 4(4):338344, Ekman P (1992) An argument for basic emotions. Social networking platforms have become an essential means for communicating feelings to the entire world due to rapid expansion in the Internet era. Soc Netw Anal Min 10:116, Nagarajan SM, Gandhi UD (2019) Classifying streaming of twitter data based on sentiment analysis using hybridization. Since existing research and applications of sentiment analysis have focused primarily on written text, it has been an active research field of natural language processing (NLP). It is a feature extraction technique wherein a document is broken down into sentences that are further broken into words; after that, the feature map or matrix is built. The training dataset is the information used to train the model by supplying the characteristics of different instances of an item. The two example sentences both expressed positive aspirations. The semantic relationships between words in traditional lexicons have not been examined, improving sentiment classification performance. 2019). In: Butz C, Lingras P (eds) Advances in artificial intelligence. The data must be processed as rapidly as generated to comprehend human psychology, and it can be accomplished using sentiment analysis, which recognizes polarity in texts. The authors used a novel approach called rich site summary for data collection and applied SVM and Nave Bayes machine learning algorithms for emotion classification of twitter text. Of course, throughout history, spoken or written communications have never had a shortage of opinions. Thus sentiment analysis research not only advances the field of NLP but also advances research in management science, political science, and economics, as these fields are all concerned with consumer and public opinions. https://doi.org/10.1007/978-981-15-6695-0_9. ICT Express 6(4):300305, Strapparava C, Valitutti A, et al. Sarcastic sentences with or without sentiment words are hard to deal with for example, What a great car! For example, Coke tastes better than Pepsi compares Coke and Pepsi based on their tastes (an aspect) and expresses a preference for Coke (see Chapter 8). Social Network Analysis and Mining Min. 2020b). A NLP researcher of any area can start to solve a corresponding problem in sentiment analysis without changing her research topic or area. They found that the logistic regression model performed better than other classifiers with a recall value of 83%. Data crawled from various social media platform's posts, blogs, e-commerce sites are usually unstructured and thus need to be processed to make it structured to reduce some additional computations outlined in the following section. Chapter 12 discusses the problem and presents an intent mining algorithm based on the idea of transfer learning (Chen et al., Reference Chen, Liu, Hsu, Castellanos and Ghosh2013a). Because debates or discussions are supposed to be exchanges of arguments and reasoning among participants who are engaged in deliberations to achieve some common goals, we can study whether each participant indeed behaves accordingly that is, by giving reasoned arguments with justifiable claims or by exhibiting dogmatism and egotistic clashes of ideologies. (Reference McGlohon, Glance and Reiter2010) used product reviews to rank products and merchants. For instance, the sentence view at this site is so serene and calm, but this place stinks shows two emotions, 'disgust' and 'soothing' in various aspects. For example, sadness and joy are opposites, but anger is not the opposite of fear. Besides academic research, some review-hosting companies filter fake reviews on their sites for example, Yelp.com and Dianping.com. (2017) applied a domain-specific lexicon for the process of feature extraction in emotion analysis. However, this technique does not contain domain specificity. Power or dominance signifies restriction over emotion. In recent years, sentiment analysis applications have spread to almost every possible domain, from consumer products, health care, tourism, hospitality, and financial services to social events and political elections. Yang etal. In that case, the system can just ignore its aspects. This level of analysis implicitly assumes that each document expresses opinions on a single entity (e.g., a single product or service). Regarding the name of the field, sentiment analysis is used almost exclusively in industry, whereas both opinion mining and sentiment analysis are commonly employed in academia. A confusion matrix is acquired, which provides the count of correct and incorrect judgments or predictions based on known actual values. Procedia Comput Sci 171:13461352, Schouten K, Frasincar F (2015) Survey on aspect-level sentiment analysis. It provides better accuracy than every other multimodal fusion technique, intending to analyze the sentiments of drug reviews written by patients on social media platforms. This matrix displays true positive (TP), false negative (FN), false positive (FP), true negative (TN) values for data fitting based on positive and negative classes. These parameters decide the position of psychological states in 2-dimensional space, as illustrated in Fig. In terms of natural language understanding, sentiment analysis can be regarded as an important subarea of semantic analysis because its goal is to recognize topics that people talk about and their sentiments toward those topics. For example, based on our commonsense knowledge, we know that I bought the car yesterday and it broke today and After sleeping on the mattress for a month, a valley has formed in the middle describe two undesirable facts, and we can safely infer that the sentence authors feel negatively about the car and the mattress, respectively. The authors built up a two-stage model based on LSTM with an attention mechanism to solve these issues. In general, a dictionary maintains words of some language systemically, whereas a corpus is a random sample of text in some language. Speech recognition, document summarization, question answering, speech synthesis, machine translation, and other applications all employ NLP (Itani etal. 2020). Sentiment and emotion analysis has a wide range of applications and can be done using various methodologies. 2 For example, delighted is more exciting than happy. In other related works, Yano and Smith (Reference Yano and Smith2010) reported a method for predicting comment volumes of political blogs, Chen et al. Finally, Sect. User's ratings and reviews on multiple platforms encourage vendors and service providers to enhance their current systems, goods, or services. (2016) proposed a feature reduction technique, a hybrid framework made of sentiment lexicon and Serbian wordnet. Owing to some of its special characteristics, sentiment analysis allows much deeper language analyses to be performed to gain better insights into NLP than in the general setting because the complexity of the general setting of NLP is simply overwhelming. Cluster Comput 22(1):11991209, Asghar MZ, Subhan F, Imran M, Kundi FM, Shamshirband S, Mosavi A, Csiba P, Vrkonyi-Kczy AR (2019) Performance evaluation of supervised machine learning techniques for efficient detection of emotions from online content. As a result, sentiment and emotion analysis has changed the way we conduct business (Bhardwaj etal. In: Computing, communication and signal processing. However, that will not work either, because a sentence can have multiple opinions for example, Apple is doing very well in this poor economy. It does not make much sense to classify this sentence as positive or negative because it is positive about Apple but negative about the economy. Sentiment analysis assists marketers in understanding their customer's perspectives better so that they may make necessary changes to their products or services (Jang etal. Lrec, Citeseer 12:38063813, Russell JA (1980) A circumplex model of affect. The authors expanded both lexicons by addition some morphological sentiment words to avoid loss of critical information while stemming. Procedia Comput Sci 179:821828, Dahou A, Xiong S, Zhou J, Haddoud MH, Duan P (2016) Word embeddings and convolutional neural network for Expert Syst Appl 40(18):74927503, Jha V, Savitha R, Shenoy PD, Venugopal K, Sangaiah AK (2018) A novel sentiment aware dictionary for multi-domain sentiment classification. In: Proceedings of the 2019 IEEE 4th international conference on computer and communication systems (ICCCS), IEEE, pp 2125, Sharma P, Sharma A (2020) Experimental investigation of automated system for twitter sentiment analysis to predict the public emotions using machine learning algorithms. Opinion mining and sentiment analysis can be, said to have brought in a large amount of interest in present day studies. Internally, agencies monitor social media to discover public sentiments and citizen concerns. (2020) proposed two models using a three-way decision theory. It makes it easy for people with hidden agendas or malicious intentions to game the system by posting fake opinions to promote or to discredit some target products, services, organizations, or individuals without disclosing their true intentions, or the person or organization for which they are secretly working. Opinions are very important to businesses and organizations because they always want to ascertain consumer or public opinions about their products and services. It is not hard to imagine that sentiment analysis using social media might profoundly change the direction of research and practice in these fields. The researchers concluded that deep neural networks such as LSTM and CNN outperformed the existing machine learning algorithms on the hotel and product review dataset. Along with these basic tasks, researchers have studied opinion summarization and opinion search, which we study in Chapter 9. Lexicon-based Approach Lexicon-based approach is a keyword-based search approach that searches for emotion keywords assigned to some psychological states (Rabeya etal. Different kinds of algorithms required for sentiment classification may include Nave Bayes, support vector machine (SVM), decision trees, etc. In the study carried out by Mohammad and Yang (Reference Mohammad2011), sentiments in males were used to find how genders differed on emotional axes. The only thing that she needs to change is the corpus, which should be an opinion corpus. For instance, stop words like "is," "at," "an," "the" have nothing to do with sentiments, so these need to be removed to avoid unnecessary computations (Bhaskar etal. Document level classifies the entire document as binary class or multi-class. Every second, a massive amount of unstructured data is generated on the Internet due to social media platforms. Sentiment analysis, the automated extraction of expressions of positive or negative. Therefore, Arora and Kansal (2019) proposed a model named Conv-char-Emb that can handle the problem of noisy data and use small memory space for embedding. 2019). Students and guardians conduct considerable online research and learn more about the potential institution, courses and professors. For example, This washer uses a lot of water implies a negative opinion about the washer because it uses a lot of resources (water). Section 2.2 describes all about emotion detection in detail. The authors achieved an accuracy of up to 87.17% with the n-gram model. A sentence containing sentiment words may not express any sentiment. In: Fong If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Sentiment analysis mainly focuses on opinions that express or imply positive or negative sentiments, also called positive or negative opinions in everyday language. However, some researchers constructed the dataset of their regional language. Sentiment analysis can be categorized at three levels, mentioned in the following section. Feature Flags: { Inthis paper, we aim to tackle the problem of sentiment polarity categorization, which isone of the fundamental problems of sentiment analysis. By orientation or polarity, we mean whether a sentiment or opinion is positive, negative, or neutral. One of the subtopicsof this research is called sentiment analysis or opinion min-ing, which is, given a bunch of text, we can computation-ally study peoples opinions, appraisals, attitudes, and emo-tions toward entities, individuals, issues, events, topics andtheir attributes. Mater Today Proc, Shaver P, Schwartz J, Kirson D, Oconnor C (1987) Emotion knowledge: further exploration of a prototype approach. Without these data, much of the existing research would not have been possible. A comparative opinion compares multiple entities based on some of their shared aspects. IEEE Access 7:111866111878, Becker K, Moreira VP, dos Santos AG (2017) Multilingual emotion classification using supervised learning: comparative experiments. 2020). Mohammads (Reference Mohammad2011) study tracked emotions in novels and fairy tales. For an organization, it may no longer be necessary to conduct surveys, opinion polls, or focus groups to gather public or consumer opinions about the organizations products and services because an abundance of such information is publicly available. S, Millham R (eds) Bio-inspired algorithms for data streaming and visualization, big data management, and fog computing, Springer Tracts in Nature-Inspired Computing. Each site typically contains a huge volume of opinion text that is not always easily deciphered from long blogs and forum posts. For example, the Young generation uses words like 'LOL,' which means laughing out loud to express laughter, 'FOMO,' which means fear of missing out, which says anxiety. The entities can be products, services, organizations, individuals, events, issues, or topics. For example, the sentence I am concerned about the current state of the economy expresses a sentiment, whereas the sentence I think the economy is not doing well expresses an opinion. Their topic-based sentiment analysis system first used a nonparametric topic model to identify daily topics related to stocks and then computed peoples sentiments about these topics. Nonetheless, in some cases, machine learning models fail to extract some implicit features or aspects of the text. For example, good, wonderful, and amazing are positive sentiment words, and bad, poor, and terrible are negative sentiment words. @free.kindle.com emails are free but can only be saved to your device when it is connected to wi-fi. Since social site's inception, educational institutes are increasingly relying on social media like Facebook and Twitter for marketing and advertising purposes. Sentiment analysis is the computational study of people's opinions, sentiments, emo-tions, and attitudes. The hybrid model achieved 87% accuracy, whereas the individual models had 75% accuracy with rule-based and 80% accuracy with the CNN model. For example, several researchers have used sentiment information to predict movie success and box-office revenue. (2019) took care of the two issues concerning aspect-level analysis: various aspects in a single sentence having different polarities and explicit position of context in an opinionated sentence. Sentiment analysis is considered an emerging topic recently. (2020) proposed the hybrid of the rule-based approach and domain lexicons for aspect-level sentiment detection to understand peoples opinions regarding government smart applications. Authors compared various word embeddings, trained using Twitter and Wikipedia as corpora with TF-IDF word embedding. A key feature of social media is that it enables people from anywhere in the world to freely express their views and opinions without disclosing their true identify and without the fear of undesirable consequences. The authors concluded that the proposed technique outperforms other lexicon-based baseline models by 5%.
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