Sentiment analysis, one of the research hotspots in the natural language processing field, has attracted the attention of researchers, and research papers on the field are increasingly published. Many literature reviews on sentiment analysis involving techniques, methods, and applications have been produced using different survey methodologies and tools, but there has not been a survey ...arXiv.org e-Print archivea sentiment label: positive, negative or neural. As mentioned, we neglect the neutral sentiments in the dataset. For data pre-processing, the following steps were taken: 1) Selecting data: There are three types of sentiments in this dataset: the positive, the negative and the neutral sentiments. Jul 15, 2020 · towards. 4-GB memory size and 2.50. GHZ processing speed. The. model also was run and tested. using three testbeds or. Sentiment model behaves better using the light stemmer. than using the ... inference, sentiment analysis, and document ranking.1. 1 Introduction Unsupervised representation learning has been highly successful in the domain of natural language processing [7, 22, 27, 28, 10]. Typically, these methods first pretrain neural networks on large-scalecriminator. It contains an original-side sentiment predictor and an antonymous-side sentiment pre-dictor, which regards the original and antonymous samples as pairs to perform dual sentiment predic-tion. 3.1 Antonymous Sentence Generator The word substitution-based methods have been shown to be effective and stable in synonymous sentence ...of sentiment consistency in Wikipedia prior to our conclusions. 2 Related Work Sentiment analysis is an important area of NLP with a large and growing literature. Excellent sur-veysoftheeldinclude(Liu, 2013; PangandLee, 2008), establishing that rich online resources have greatly expanded opportunities for opinion min-ing and sentiment analysis.has been applied to cross-lingual sentiment (Zhou et al., 2016), aspect-level sentiment (Wang et al., 2016) and user-oriented sentiment (Chen et al., 2016). To our knowledge, we are the rst to use the attention mechanism to model sentences with respect to targeted sentiments. 3 Models We use a bidirectional LSTM to represent the in- sentiment (e.g., That’s a girl I know.) They also included factual questions, commercial information, plot summaries, descriptions, etc.. We opted to not define a separate “mixed sentiment” class, as this would not be particularly useful, and is also difficult for models to capture (Liu, 2015, p. 77). All cases of mixed sentiment were ...sentiment polarity (i.e., positive, neutral and negative) of the opinion target tin the sentence s. DSC Formalization For a review document dfrom the DSC dataset D, we regard it as a special long sentence fwd 1;w d 2;:::;w d ngconsisting of nwords. DSC aims to determine the overall sentiment polarity of the review document d. 2.2 Pre-trainig ... For document-level sentiment classification, the best per-forming system reached a micro-averaged F 1 score of 74.9. This approach (Naderalvojoud et al., 2017) is particularly interesting because it incorporates information from exis-ting sentiment lexica into a neural network architecture. Schmitt et al. (2018) published the GermEval-2017 ...sentiment polarity (i.e., positive, neutral and negative) of the opinion target tin the sentence s. DSC Formalization For a review document dfrom the DSC dataset D, we regard it as a special long sentence fwd 1;w d 2;:::;w d ngconsisting of nwords. DSC aims to determine the overall sentiment polarity of the review document d. 2.2 Pre-trainig ... Cyberpunk 2077 is an open-world, action-adventure RPG set in the megalopolis of Night City, where you play as a cyberpunk mercenary wrapped up in a do-or-die fight for survival. Improved and featuring all-new free additional content, customize your character and playstyle as you take on jobs, build a reputation, and unlock upgrades.on a scale from 1-5). The sentiment of text is a measure of the speaker’s tone, attitude, or evaluation of a topic, independent of the topic’s own sentiment orientation (e.g., a horror movie can be \delightful.") Sentiment analysis is a well-studied subject in computational text analysis and has a correspondingly rich history of prior work. 2level sentiments with word-level sentiments by pro-gressively contrasting a sentence with missing sen-timents to a supercially similar sentence. 3.1 Word-Level Pre-training Word masking Different from previous random word masking (Devlin et al.,2019;Clark et al., 2020), our goal is to corrupt the sentiment of the input sentence. One of the key challenges in sentiment analysis is to model compositional sentiment semantics. Take the sentence “Frenetic but not really funny.” in Fig-ure 1 as an example. The two parts of the sentence are connected by “but”, which reveals the change of sentiment. Besides, the word “not” changes the sentiment of “really funny ...paper: sentiment lexicon, negation words, and in-tensity words. Sentiment lexicon offers the prior polarity of a word which can be useful in deter-mining the sentiment polarity of longer texts such asphrasesandsentences. Negatorsaretypicalsen-timentshifters(Zhuetal.,2014),whichconstantly change the polarity of sentiment expression. In-One of the key challenges in sentiment analysis is to model compositional sentiment semantics. Take the sentence “Frenetic but not really funny.” in Fig-ure 1 as an example. The two parts of the sentence are connected by “but”, which reveals the change of sentiment. Besides, the word “not” changes the sentiment of “really funny ...Wir werden zunächst einen Blick auf das EPR-Argument und die Anfänge der Debatte um verschränkte Zustände werfen (Abschn. 4.2 ). In den folgenden Abschnitten werden wir dann die aktuelle Debatte um Verschränkung und Nicht-Lokalität darstellen, die vor allem auf Bells Beweis und einschlägigen Experimenten beruht.Supervised contrastive learning gives an aligned representation of sentiment expressions with the same sentiment label. In embedding space, explicit and implicit sentiment expressions with the same sentiment orientation are pulled together, and those with different sentiment labels are pushed apart.Conflicting sentiment labels are a natural occurrence. We propose using a simple majority voting scheme to select the most probably sentiment label as the ground-truth. Based on this approach, the corpus has 30.4% positive utterances, 17% negative utterances, and 52.6% neutral utterances. Us-ing the highest voted sentiment label as ground ...Data Inquiries Media Inquiries . International Trade Indicator Branch: 301-763-2311 [email protected] Public Information Office sentiment classication. Though being effec-tive, such methods rely on external depen-dency parsers, which can be unavailable for low-resource languages or perform worse in low-resourcedomains. Inaddition,dependency trees are also not optimized for aspect-based sentiment classication. In this paper, we pro-pose an aspect-specic and language-agnostic tic/syntactic and sentiment information such that sentimentally similar words have similar vector representations. They typically apply an objective function to optimize word vectors based on the sentiment polarity labels (e.g., positive and nega-tive) given by the training instances. The use of such sentiment embeddings has improved the per-Angst, 0,78 für Vermeidung und 0,60 für physiologische Erre-gung. Um die konvergente Validität zu erheben, wurde die BSPS mit der Æ LSAS, der Æ Skala „Angst vor negativer Bewertung“ Commonly known as the Beige Book, this report is published eight times per year. Each Federal Reserve Bank gathers anecdotal information on current economic conditions in its District through reports from Bank and Branch directors and interviews with key business contacts, economists, market experts, and other sources.Smith on Moral Sentiments Sympathy Part I: The Propriety of Action Section 1: The Sense of Propriety Chapter 1: Sympathy No matter how selfish you think man is, it’s obvious that level sentiments with word-level sentiments by pro-gressively contrasting a sentence with missing sen-timents to a supercially similar sentence. 3.1 Word-Level Pre-training Word masking Different from previous random word masking (Devlin et al.,2019;Clark et al., 2020), our goal is to corrupt the sentiment of the input sentence. Mar 23, 2016 · SAOM is an active field of research and an interdisciplinary area that includes text mining, Natural Language Processing (NLP), and data mining [5]. Sentiment analysis and opinion mining tasks are ... based sentiment classication solutions. 1 Introduction Sentiment is personal; the same sentiment can be expressed in various ways and the same expres-sion might carry distinct polarities across different individuals (Wiebe et al., 2005). Current main-stream solutions of sentiment analysis overlook this fact by focusing on population-level modelsuses document-level sentiment annotations to constrain words expressing similar sentiment to have simi-lar representations. Tang et al. (2014) changed the objective function of the C&W (Collobert et al., 2011) model to produce sentiment-specific word vectors for Twitter sentiment analysis, by leveraging large vol-umes of distant-supervised tweets. Mar 23, 2016 · SAOM is an active field of research and an interdisciplinary area that includes text mining, Natural Language Processing (NLP), and data mining [5]. Sentiment analysis and opinion mining tasks are ... of sentiment consistency in Wikipedia prior to our conclusions. 2 Related Work Sentiment analysis is an important area of NLP with a large and growing literature. Excellent sur-veysoftheeldinclude(Liu, 2013; PangandLee, 2008), establishing that rich online resources have greatly expanded opportunities for opinion min-ing and sentiment analysis.necessarily cover the sentiment expressed by the author towards a specific entity. To address this gap, we introduce PerSenT, a crowdsourced dataset of sentiment annotations on news articles about people. For each article, annotators judge what the author’s sentiment is towards the main (target) entity of the article.Moralia. The Moralia ( Ancient Greek: Ἠθικά Ethika; loosely translated as "Morals" or "Matters relating to customs and mores") is a group of manuscripts written in Ancient Greek, dating from the 10th–13th centuries, and traditionally ascribed to the 1st-century scholar Plutarch of Chaeronea. [1] The eclectic collection contains 78 ... a sentiment label: positive, negative or neural. As mentioned, we neglect the neutral sentiments in the dataset. For data pre-processing, the following steps were taken: 1) Selecting data: There are three types of sentiments in this dataset: the positive, the negative and the neutral sentiments.Smith on Moral Sentiments Sympathy Part I: The Propriety of Action Section 1: The Sense of Propriety Chapter 1: Sympathy No matter how selfish you think man is, it’s obvious that Selected sentiment datasetsLexica Tokenizing The dangers of stemming Other preprocessing techniques Selected sentiment datasets There are too many to try to list, so I picked some with noteworthy properties, limiting to the core task of sentiment analysis: • IMDb movie reviews (50K) (Maas et al. 2011): Trend- und Sentiment-Analyse des Begriffs‚ndustrie 4.0‘− Social Media-Monitoring von Innovationskommunikation Volker M. Banholzer..... 161 Die Bedeutung der Digitalisierung in der arbeitsmarktgerichteten Unternehmenskommunikation– eine explorative Stellenanzeigen- sentiment polarity for each aspect. However, when taken the context into consideration, the sentiment polarity for each aspect in S2 is largely possible to be positive, since all the neighboring sentences express the positive sentiment polarity for their as-pects. Therefore, a well-behaved model should capture the contextual sentiment tendency ...user sentiments towards products, by analyzing user-generated natural language text content. 2 Related Work Sentiment analysis (SA) has been an area of long-standing area of research. A seminal work was carried out byHatzivassiloglou and McKeown (1997), attempting to identify the sentiment po-larity orientation of adjectives, using conjunctionAuthors:Ziqian Zeng, Yangqiu Song. Download a PDF of the paper titled Variational Weakly Supervised Sentiment Analysis with Posterior Regularization, by Ziqian Zeng and 1 other authors. Download PDF. Abstract:Sentiment analysis is an important task in natural language processing (NLP).sentiment modification, treating it as a cloze form task of filling in the appropriate words in the target sentiment. In contrast, we are capable of generating the entire sentence in the target style. Further, our work is more generalizable and we show results on five other style transfer tasks. 3 Tasks and Datasets 3.1 Politeness Transfer Tasking sentiment polarity (s), and the opinion term (o). For example, in the sentence “Thedrinksare al-wayswell madeandwine selectionisfairly priced”, the aspect terms are “drinks” and “wine selection”, and their sentiment polarities are both “positive”, and the opinion terms are “well made” and “fairly priced”. 3 Sentiment Analysis Two different approaches of sentiment analysis can be identied. The rst approach uses lexicons to retrieve the sentiment polarity of a text. This lexicons contain dictionaries of positive, negative, and neutral words and the sentiment polarity is re-trieved according to the words in a text. Machineing sentiment polarity (s), and the opinion term (o). For example, in the sentence “Thedrinksare al-wayswell madeandwine selectionisfairly priced”, the aspect terms are “drinks” and “wine selection”, and their sentiment polarities are both “positive”, and the opinion terms are “well made” and “fairly priced”.Word2vec is a technique for natural language processing (NLP) published in 2013. The word2vec algorithm uses a neural network model to learn word associations from a large corpus of text. Once trained, such a model can detect synonymous words or suggest additional words for a partial sentence.May 28, 2020 · Here we show that scaling up language models greatly improves task-agnostic, few-shot performance, sometimes even reaching competitiveness with prior state-of-the-art fine-tuning approaches. Specifically, we train GPT-3, an autoregressive language model with 175 billion parameters, 10x more than any previous non-sparse language model, and test ... Sentiment Analysis in Social Networks. Morgan Kaufmann, S. 4. Aspect-based sentiment classification. Contextual polarity disambiguation. Sentiment ratingprediction. Cross -domain sentiment classification. Cross -languagesentiment classification. Subjectivity classification. Polarity classification. Opinion summarization. Opinion visualization ...Moralia. The Moralia ( Ancient Greek: Ἠθικά Ethika; loosely translated as "Morals" or "Matters relating to customs and mores") is a group of manuscripts written in Ancient Greek, dating from the 10th–13th centuries, and traditionally ascribed to the 1st-century scholar Plutarch of Chaeronea. [1] The eclectic collection contains 78 ...Supervised contrastive learning gives an aligned representation of sentiment expressions with the same sentiment label. In embedding space, explicit and implicit sentiment expressions with the same sentiment orientation are pulled together, and those with different sentiment labels are pushed apart.Sentiment Lexica 2.1. Existing Danish Sentiment Resources To our knowledge, Afinn was the first freely available sentiment resource for Danish and is described together with other resources in Nielsen (2020). This senti-ment list is a translation and customization of an ex-isting English sentiment lexicon (Nielsen, 2011). The words provided in a sentiment lexicon and a lexicon-based classifier to perform sentiment analysis. One major issue with this approach is that many sentiment words (from the lexicon) are domain dependent. That is, they may be positive in some domains but negative in some others. We refer to this problem as domain polarity-changes of words from ... tic/syntactic and sentiment information such that sentimentally similar words have similar vector representations. They typically apply an objective function to optimize word vectors based on the sentiment polarity labels (e.g., positive and nega-tive) given by the training instances. The use of such sentiment embeddings has improved the per-sentiment categorization, the shape of the under-lying continuous sentiment distribution would be unknown. In fact, all distributions shown on the left hand side in Figure1produce the plot on the right hand side in Figure1if the sentiment values are binarized in such way that tweets with a sen-timent value of 0.5 are assigned to the positive Sentiment Lexica 2.1. Existing Danish Sentiment Resources To our knowledge, Afinn was the first freely available sentiment resource for Danish and is described together with other resources in Nielsen (2020). This senti-ment list is a translation and customization of an ex-isting English sentiment lexicon (Nielsen, 2011). TheWord2vec is a technique for natural language processing (NLP) published in 2013. The word2vec algorithm uses a neural network model to learn word associations from a large corpus of text. Once trained, such a model can detect synonymous words or suggest additional words for a partial sentence.OverviewMaterialsConceptual challenges Sentiment analysis in industry Affective computingOur primary datasets Overview of this unit 1.Sentiment as a deep and important NLU problem 2.General practical tips for sentiment analysis 3.The Stanford Sentiment Treebank (SST) 4.The DynaSent dataset 5.sst.py 6.Methods: hyperparameters and classifier ...3 Sentiment Analysis Two different approaches of sentiment analysis can be identied. The rst approach uses lexicons to retrieve the sentiment polarity of a text. This lexicons contain dictionaries of positive, negative, and neutral words and the sentiment polarity is re-trieved according to the words in a text. Machine of sentiment consistency in Wikipedia prior to our conclusions. 2 Related Work Sentiment analysis is an important area of NLP with a large and growing literature. Excellent sur-veysoftheeldinclude(Liu, 2013; PangandLee, 2008), establishing that rich online resources have greatly expanded opportunities for opinion min-ing and sentiment analysis.Sentiment analysis is the computational study of people窶冱 opinions, sentiments, emo- tions,andattitudes.Thisfascinatingproblemisincreasinglyimportantinbusinessand society. It offers numerous research challenges but promises insight useful to anyone interested in opinion analysis and social media analysis.for our tareget-based sentiment annoation corpus, namely target entities and sentiment polarity of each target entity. For assisting annotators in better understanding sentiment and annotation checking, we need also annotate the senti-ment expression clauses. Target entity annotation Enterprises are the subject in economic activities. Thus,sentiment polarity for each aspect. However, when taken the context into consideration, the sentiment polarity for each aspect in S2 is largely possible to be positive, since all the neighboring sentences express the positive sentiment polarity for their as-pects. Therefore, a well-behaved model should capture the contextual sentiment tendency ...has been applied to cross-lingual sentiment (Zhou et al., 2016), aspect-level sentiment (Wang et al., 2016) and user-oriented sentiment (Chen et al., 2016). To our knowledge, we are the rst to use the attention mechanism to model sentences with respect to targeted sentiments. 3 Models We use a bidirectional LSTM to represent the in- Sentiment analysis granularity is subdivided into document level, sentence level, and aspect level. Document-level sentiment analysis takes the entire document as a unit, but the premise is that the document needs to have a clear attitude orientation—that is, the point of view needs to be clear (Shirsat et al. 2018; Wang and Wan 2011).By. Elizabeth Wagmeister. It’s teatime in London, and Olivia Wilde is talking about the O-word. No, not the Oscars, but her approach to sex scenes in her new movie, “Don’t Worry Darling ...sentiment modification, treating it as a cloze form task of filling in the appropriate words in the target sentiment. In contrast, we are capable of generating the entire sentence in the target style. Further, our work is more generalizable and we show results on five other style transfer tasks. 3 Tasks and Datasets 3.1 Politeness Transfer Tasksentiment classication. Though being effec-tive, such methods rely on external depen-dency parsers, which can be unavailable for low-resource languages or perform worse in low-resourcedomains. Inaddition,dependency trees are also not optimized for aspect-based sentiment classication. In this paper, we pro-pose an aspect-specic and language-agnosticPerceived social isolation (PSI) is associated with substantial morbidity and mortality. Social media platforms, commonly used by young adults, may offer an opportunity to ameliorate social isolation. This study assessed associations between social media use (SMU) and PSI among U.S. young adults.2013). The next stage of our sentiment detection is the verb resource, which was also implemented with the vislcg3 tools and will be explained in the next section. 3.2 Verb-based Sentiment Analysis In order to combine the composition of the po-lar phrases with verb information, we encoded the impact of the verbs on polarity using three di- sentiment polarity (i.e., positive, neutral and negative) of the opinion target tin the sentence s. DSC Formalization For a review document dfrom the DSC dataset D, we regard it as a special long sentence fwd 1;w d 2;:::;w d ngconsisting of nwords. DSC aims to determine the overall sentiment polarity of the review document d. 2.2 Pre-trainig ...2013). The next stage of our sentiment detection is the verb resource, which was also implemented with the vislcg3 tools and will be explained in the next section. 3.2 Verb-based Sentiment Analysis In order to combine the composition of the po-lar phrases with verb information, we encoded the impact of the verbs on polarity using three di-Moralia. The Moralia ( Ancient Greek: Ἠθικά Ethika; loosely translated as "Morals" or "Matters relating to customs and mores") is a group of manuscripts written in Ancient Greek, dating from the 10th–13th centuries, and traditionally ascribed to the 1st-century scholar Plutarch of Chaeronea. [1] The eclectic collection contains 78 ...Selected sentiment datasetsLexica Tokenizing The dangers of stemming Other preprocessing techniques Selected sentiment datasets There are too many to try to list, so I picked some with noteworthy properties, limiting to the core task of sentiment analysis: • IMDb movie reviews (50K) (Maas et al. 2011): a sentiment label: positive, negative or neural. As mentioned, we neglect the neutral sentiments in the dataset. For data pre-processing, the following steps were taken: 1) Selecting data: There are three types of sentiments in this dataset: the positive, the negative and the neutral sentiments. Selected sentiment datasetsLexica Tokenizing The dangers of stemming Other preprocessing techniques Selected sentiment datasets There are too many to try to list, so I picked some with noteworthy properties, limiting to the core task of sentiment analysis: • IMDb movie reviews (50K) (Maas et al. 2011):uses document-level sentiment annotations to constrain words expressing similar sentiment to have simi-lar representations. Tang et al. (2014) changed the objective function of the C&W (Collobert et al., 2011) model to produce sentiment-specific word vectors for Twitter sentiment analysis, by leveraging large vol-umes of distant-supervised tweets.of sentiment consistency in Wikipedia prior to our conclusions. 2 Related Work Sentiment analysis is an important area of NLP with a large and growing literature. Excellent sur-veysoftheeldinclude(Liu, 2013; PangandLee, 2008), establishing that rich online resources have greatly expanded opportunities for opinion min-ing and sentiment analysis.for our tareget-based sentiment annoation corpus, namely target entities and sentiment polarity of each target entity. For assisting annotators in better understanding sentiment and annotation checking, we need also annotate the senti-ment expression clauses. Target entity annotation Enterprises are the subject in economic activities. Thus,
express positive sentiment Table 1: Examples of tweets with vulgar words and their function. Does vulgarity impact perception of sentiment? Does modeling vulgarity explicitly help sentiment prediction? To this end, we collect a new data set of 6.8K tweets labeled for sentiment on a five-point scale by nine annotators.. P f chang
to predict the sentiment score. We conduct experiments on two multimodal sentiment analysis benchmarks: CMU-MOSI and CMU-MOSEI. The experimental results show that our model outperforms all baselines. This can demonstrate that the shared-private framework for multimodal sentiment analysis can explicitly use the shared semantics between different ...sentiment classication, and indicates AMR is ben-ecial for simplied clause generation. 2 Related Work In this study, we introduce two related topics of this study: document-level sentiment classication and text simplication. 2.1 Sentiment Classication Intheliterature,variousstudiesfocusondocument-level sentiment classication (Pang et al.,2002; Abstract. This paper demonstrates how a graph-based semantic parser can be applied to the task of structured sentiment analysis, directly predicting sentiment graphs from text. We advance the state of the art on 4 out of 5 standard benchmark sets. We release the source code, models and predictions. Anthology ID:sentiment categorization, the shape of the under-lying continuous sentiment distribution would be unknown. In fact, all distributions shown on the left hand side in Figure1produce the plot on the right hand side in Figure1if the sentiment values are binarized in such way that tweets with a sen-timent value of 0.5 are assigned to the positiveAuthors:Ziqian Zeng, Yangqiu Song. Download a PDF of the paper titled Variational Weakly Supervised Sentiment Analysis with Posterior Regularization, by Ziqian Zeng and 1 other authors. Download PDF. Abstract:Sentiment analysis is an important task in natural language processing (NLP).Title Analyse Sentiment of English Sentences Version 2.2.2 Imports plyr,stringr,openNLP,NLP Date 2018-07-27 Author Subhasree Bose <[email protected]> with contributons from Saptarsi Goswami. Maintainer Subhasree Bose <[email protected]> Description Analyses sentiment of a sentence in English and assigns score to it. It can classify sen-Formal executions of protesters follow trials human rights groups regard as shams. Thousands are in jail, many subject to horrific torture. The regime paints what is an emphatic grassroots expression of popular anti-government sentiment, particularly among youth and in long-neglected peripheries, as a foreign plot. Few buy it.has been applied to cross-lingual sentiment (Zhou et al., 2016), aspect-level sentiment (Wang et al., 2016) and user-oriented sentiment (Chen et al., 2016). To our knowledge, we are the rst to use the attention mechanism to model sentences with respect to targeted sentiments. 3 Models We use a bidirectional LSTM to represent the in- OverviewMaterialsConceptual challenges Sentiment analysis in industry Affective computingOur primary datasets Overview of this unit 1.Sentiment as a deep and important NLU problem 2.General practical tips for sentiment analysis 3.The Stanford Sentiment Treebank (SST) 4.The DynaSent dataset 5.sst.py 6.Methods: hyperparameters and classifier ... a sentiment label: positive, negative or neural. As mentioned, we neglect the neutral sentiments in the dataset. For data pre-processing, the following steps were taken: 1) Selecting data: There are three types of sentiments in this dataset: the positive, the negative and the neutral sentiments.Abstract. This paper demonstrates how a graph-based semantic parser can be applied to the task of structured sentiment analysis, directly predicting sentiment graphs from text. We advance the state of the art on 4 out of 5 standard benchmark sets. We release the source code, models and predictions. Anthology ID:to predict the sentiment score. We conduct experiments on two multimodal sentiment analysis benchmarks: CMU-MOSI and CMU-MOSEI. The experimental results show that our model outperforms all baselines. This can demonstrate that the shared-private framework for multimodal sentiment analysis can explicitly use the shared semantics between different ...inference, sentiment analysis, and document ranking.1. 1 Introduction Unsupervised representation learning has been highly successful in the domain of natural language processing [7, 22, 27, 28, 10]. Typically, these methods first pretrain neural networks on large-scaleSentiment analysis is the computational study of people窶冱 opinions, sentiments, emo- tions,andattitudes.Thisfascinatingproblemisincreasinglyimportantinbusinessand society. It offers numerous research challenges but promises insight useful to anyone interested in opinion analysis and social media analysis. Abstract: This paper investigates how investor sentiment a ects stock market returns and evaluates the predictability power of sentiment indices on U.S. and EU stock market returns. As regards the American example, evidence shows that investor sentiment indices have an economic and statistical predictability power on stock market returns.In this paper, from defining the sentiment analysis to algorithms for sentiment analysis and from the first step of sentiment analysis to evaluating the predictions of sentiment classifiers, additional feature extractions to boost performance are discussed with practical results.a sentiment lexicon with sentiment-aware wordembedding. However,thesemethod-s were normally trained under document-level sentiment supervision. In this paper, we develop a neural architecture to train a sentiment-aware word embedding by inte-grating the sentiment supervision at both document and word levels, to enhance the .