The presented work aims at generating a systematically annotated corpus that can support the enhancement of sentiment analysis tasks in Telugu using word-level sentiment annotations. From OntoSenseNet, we extracted 11,000 adjectives, 253 adverbs, 8483 verbs and sentiment annotation is being done by language experts. We discuss the methodology followed for the polarity annotations and validate the developed resource. This work aims at developing a benchmark corpus, as an extension to SentiWordNet, and baseline accuracy for a model where lexeme annotations are applied for sentiment predictions. The fundamental aim of this paper is to validate and study the possibility of utilizing machine learning algorithms, word-level sentiment annotations in the task of automated sentiment identification. Furthermore, accuracy is improved by annotating the bi-grams extracted from the target corpus.
Sentiment Analysis: Several approaches have been proposed to capture the sentiment in the text where each approach addresses the issue at different levels of granularity. Some researchers have proposed methods for document-level sentiment classification @cite_2 @cite_9 . At the top level of granularity, it is often impossible to infer the sentiment expressed about any particular entity, because a document may convey different opinions for different entities. Hence, when we consider the tasks of opinion mining where the sole aim is to capture the sentiment polarities about entities, such as products in product reviews, it has been shown that sentence-level and phrase-level analysis lead to a performance gain @cite_5 @cite_7 . In the context of Indian languages, @cite_1 proposes an alternate way to build the resources for multilingual affect analysis where translations into Telugu are done using WordNet.
@cite_7 @cite_9 @cite_1 @cite_2 @cite_5
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The discipline where sentiment opinion emotion has been identified and classified in human written text is well known as sentiment analysis. A typical computational approach to sentiment analysis starts with prior polarity lexicons where entries are tagged with their prior out of context polarity as human beings perceive using their cognitive knowledge. Till date, all research efforts found in sentiment lexicon literature deal mostly with English texts. In this article, we propose multiple computational techniques like, WordNet based, dictionary based, corpus based or generative approaches for generating SentiWordNet(s) for Indian languages. Currently, SentiWordNet(s) are being developed for three Indian languages: Bengali, Hindi and Telugu. An online intuitive game has been developed to create and validate the developed SentiWordNet(s) by involving Internet population. A number of automatic, semi-automatic and manual validations and evaluation methodologies have been adopted to measure the coverage and credibility of the developed SentiWordNet(s). Sentiment Analysis (SA) research has gained tremendous momentum in recent times. However, there has been little work in this area for an Indian language. We propose in this paper a fall-back strategy to do sentiment analysis for Hindi documents, a problem on which, to the best of our knowledge, no work has been done until now. (A) First of all, we study three approaches to perform SA in Hindi. We have developed a sentiment annotated corpora in the Hindi movie review domain. The first of our approaches involves training a classifier on this annotated Hindi corpus and using it to classify a new Hindi document. (B) In the second approach, we translate the given document into English and use a classifier trained on standard English movie reviews to classify the document. (C) In the third approach, we develop a lexical resource called Hindi-SentiWordNet (H-SWN) and implement a majority score based strategy to classify the given document. Abstract Aspect-based sentiment analysis aims at identifying sentiment polarity towards aspect targets in a sentence. Previously, the task was modeled as a sentence-level sentiment classification problem that treated aspect targets as a hint. Such approaches oversimplify the problem by averaging word embeddings when the aspect target is a multi-word sequence. In this paper, we formalize the problem from a different perspective, i.e., that sentiment at aspect target level should be the main focus. Due to the fact that written Chinese is very rich and complex, Chinese aspect targets can be studied at three different levels of granularity: radical, character and word. Thus, we propose to explicitly model the aspect target and conduct sentiment classification directly at the aspect target level via three granularities. Moreover, we study two fusion methods for such granularities in the task of Chinese aspect-level sentiment analysis. Experimental results on a multi-word aspect target subset from SemEval2014 and four Chinese review datasets validate our claims and show the improved performance of our model over the state of the art. Sentiment analysis is the task of determining the attitude (positive or negative) of documents. While the polarity of words in the documents is informative for this task, polarity of some words cannot be determined without domain knowledge. Detecting word polarity thus poses a challenge for multiple-domain sentiment analysis. Previous approaches tackle this problem with transfer learning techniques, but they cannot handle multiple source domains and multiple target domains. This paper proposes a novel Bayesian probabilistic model to handle multiple source and multiple target domains. In this model, each word is associated with three factors: Domain label, domain dependence independence and word polarity. We derive an efficient algorithm using Gibbs sampling for inferring the parameters of the model, from both labeled and unlabeled texts. Using real data, we demonstrate the effectiveness of our model in a document polarity classification task compared with a method not considering the differences between domains. Moreover our method can also tell whether each word's polarity is domain-dependent or domain-independent. This feature allows us to construct a word polarity dictionary for each domain.