Estimating the intensity of emotion has gained significance as modern textual inputs in potential applications like social media, e-retail markets, psychology, advertisements etc., carry a lot of emotions, feelings, expressions along with its meaning. However, the approaches of traditional sentiment analysis primarily focuses on classifying the sentiment in general (positive or negative) or at an aspect level(very positive, low negative, etc.) and cannot exploit the intensity information. Moreover, automatically identifying emotions like anger, fear, joy, sadness, disgust etc., from text introduces challenging scenarios where single tweet may contain multiple emotions with different intensities and some emotions may even co-occur in some of the tweets. In this paper, we propose an architecture, Experts Model, inspired from the standard Mixture of Experts (MoE) model. The key idea here is each expert learns different sets of features from the feature vector which helps in better emotion detection from the tweet. We compared the results of our Experts Model with both baseline results and top five performers of SemEval-2018 Task-1, Affect in Tweets (AIT). The experimental results show that our proposed approach deals with the emotion detection problem and stands at top-5 results.
In literature, there has been an increasing focus towards building sentiment classification prediction models through various approaches like rule mining, machine learning or deep learning. A brief overview of the efforts of scientific community towards sentiment related models can be found in @cite_38 @cite_12 @cite_6 @cite_27 .
@cite_38 @cite_27 @cite_6 @cite_12
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Sentiment Classification seeks to identify a piece of text according to its author's general feeling toward their subject, be it positive or negative. Traditional machine learning techniques have been applied to this problem with reasonable success, but they have been shown to work well only when there is a good match between the training and test data with respect to topic. This paper demonstrates that match with respect to domain and time is also important, and presents preliminary experiments with training data labeled with emoticons, which has the potential of being independent of domain, topic and time. The internet has made it feasible to tap a continuous stream of public sentiment from the world wide web, quite literally permitting one to "feel the pulse" of any issue under consideration. We present a methodology for real time sentiment extraction in the domain of finance. With the advent of the web, there has been a sharp increase in the influence of individuals on the stock market via web-based trading and the posting of sentiment to stock message boards. While it is importantto capture this "sentiment" of small investors, as yet, no index of sentiment has been compiled. This paper comprises (a) a technology for extracting small investor sentiment from web sources to create an index, and (b) illustrative applications of the methodology. We make use of computerized natural language and statistical algorithms for the automated classification of messages posted on the web. We design a suite of classification algorithms, each of different theoretical content, with a view to characterizing the sentiment of any single posting to a message board. The use of multiple methods allows imposition of voting rules in the classification process. It also enables elimination of "fuzzy" messages which are better off uninterpreted. A majority rule across algorithms vastly improves classification accuracy, but also leads to a natural increase in the number of messages classified as "fuzzy". The classifier achieves an accuracy of 62 (versus a random classification accuracy of 33 ), and compares favorably against human agreement on message classification, which was 72 . The technology is computationally efficient, allowing the access and interpretations of thousands of messages within minutes. Our illustrative applications show evidence of a strong link between market movements and sentiment. Based on approximately 25,000 messages for the last quarter of 2000, we found evidence that sentiment is based on stock movements. Sentiment analysis concerns about automatically identifying sentiment or opinion expressed in a given piece of text. Most prior work either use prior lexical knowledge defined as sentiment polarity of words or view the task as a text classification problem and rely on labeled corpora to train a sentiment classifier. While lexicon-based approaches do not adapt well to different domains, corpus-based approaches require expensive manual annotation effort. In this paper, we propose a novel framework where an initial classifier is learned by incorporating prior information extracted from an existing sentiment lexicon with preferences on expectations of sentiment labels of those lexicon words being expressed using generalized expectation criteria. Documents classified with high confidence are then used as pseudo-labeled examples for automatical domain-specific feature acquisition. The word-class distributions of such self-learned features are estimated from the pseudo-labeled examples and are used to train another classifier by constraining the model's predictions on unlabeled instances. Experiments on both the movie-review data and the multi-domain sentiment dataset show that our approach attains comparable or better performance than existing weakly-supervised sentiment classification methods despite using no labeled documents. Sentiment analysis, also called opinion mining, is a form of information extraction from text of growing research and commercial interest. In this paper we present our machine learning experiments with regard to sentiment analysis in blog, review and forum texts found on the World Wide Web and written in English, Dutch and French. We train from a set of example sentences or statements that are manually annotated as positive, negative or neutral with regard to a certain entity. We are interested in the feelings that people express with regard to certain consumption products. We learn and evaluate several classification models that can be configured in a cascaded pipeline. We have to deal with several problems, being the noisy character of the input texts, the attribution of the sentiment to a particular entity and the small size of the training set. We succeed to identify positive, negative and neutral feelings to the entity under consideration with ca. 83 accuracy for English texts based on unigram features augmented with linguistic features. The accuracy results of processing the Dutch and French texts are ca. 70 and 68 respectively due to the larger variety of the linguistic expressions that more often diverge from standard language, thus demanding more training patterns. In addition, our experiments give us insights into the portability of the learned models across domains and languages. A substantial part of the article investigates the role of active learning techniques for reducing the number of examples to be manually annotated.