An Approach for Big Data to Evolve the Auspicious Information from Cross-Domains

Sentiment analysis is the pre-eminent technology to extract the relevant information from the data domain. In this paper cross domain sentimental classification approach Cross_BOMEST is proposed. Proposed approach will extract †ve words using existing BOMEST technique, with the help of Ms Word Introp, Cross_BOMEST determines †ve words and replaces all its synonyms to escalate the polarity and blends two different domains and detects all the self-sufficient words. Proposed Algorithm is executed on Amazon datasets where two different domains are trained to analyze sentiments of the reviews of the other remaining domain. Proposed approach contributes propitious results in the cross domain analysis and accuracy of 92 % is obtained. Precision and Recall of BOMEST is improved by 16% and 7% respectively by the Cross_BOMEST

HMDSAD: Hindi multi-domain sentiment aware dictionary

Sentiment Analysis is a fast growing sub area of Natural Language Processing which extracts user's opinion and classify it according to its polarity into positive, negative or neutral classes. This task of classification is required for many purposes like opinion mining, opinion summarization, contextual advertising and market analysis but it is domain dependent. The words used to convey sentiments in one domain is different from the words used to express sentiments in other domain and it is a costly task to annotate the corpora in every possible domain of interest before training the classifier for the classification. We are making an attempt to solve this problem by creating a sentiment aware dictionary using multiple domain data. The source domain data is labeled into positive and negative classes at the document level and the target domain data is unlabeled. The dictionary is created using both source and target domain data. The words used to express positive or negative sentiments in labeled data has relatedness weights assigned to it which signifies its co-occurrence frequency with the words expressing the similar sentiments in target domain. This work is carried out in Hindi, the official language of India. The web pages in Hindi language is booming very quickly after the introduction of UTF-8 encoding style. The dictionary can be used to classify the unlabeled data in the target domain by training a classifier

Cross-domain sentiment classification using a sentiment sensitive thesaurus

Automatic classification of sentiment is important for numerous applications such as opinion mining, opinion summarization, contextual advertising, and market analysis. However, sentiment is expressed differently in different domains, and annotating corpora for every possible domain of interest is costly. Applying a sentiment classifier trained using labeled data for a particular domain to classify sentiment of user reviews on a different domain often results in poor performance. We propose a method to overcome this problem in cross-domain sentiment classification. First, we create a sentiment sensitive distributional thesaurus using labeled data for the source domains and unlabeled data for both source and target domains. Sentiment sensitivity is achieved in the thesaurus by incorporating document level sentiment labels in the context vectors used as the basis for measuring the distributional similarity between words. Next, we use the created thesaurus to expand feature vectors during train and test times in a binary classifier. The proposed method significantly outperforms numerous baselines and returns results that are comparable with previously proposed cross-domain sentiment classification methods. We conduct an extensive empirical analysis of the proposed method on single and multi-source domain adaptation, unsupervised and supervised domain adaptation, and numerous similarity measures for creating the sentiment sensitive thesaurus

A Cross-Lingual Similarity Measure for Detecting Biomedical Term Translations

Bilingual dictionaries for technical terms such as biomedical terms are an important resource for machine translation systems as well as for humans who would like to understand a concept described in a foreign language. Often a biomedical term is first proposed in English and later it is manually translated to other languages. Despite the fact that there are large monolingual lexicons of biomedical terms, only a fraction of those term lexicons are translated to other languages. Manually compiling large-scale bilingual dictionaries for technical domains is a challenging task because it is difficult to find a sufficiently large number of bilingual experts. We propose a cross-lingual similarity measure for detecting most similar translation candidates for a biomedical term specified in one language (source) from another language (target). Specifically, a biomedical term in a language is represented using two types of features: (a) intrinsic features that consist of character n-grams extracted from the term under consideration, and (b) extrinsic features that consist of unigrams and bigrams extracted from the contextual windows surrounding the term under consideration. We propose a cross-lingual similarity measure using each of those feature types. First, to reduce the dimensionality of the feature space in each language, we propose prototype vector projection (PVP)—a non-negative lower-dimensional vector projection method. Second, we propose a method to learn a mapping between the feature spaces in the source and target language using partial least squares regression (PLSR). The proposed method requires only a small number of training instances to learn a cross-lingual similarity measure. The proposed PVP method outperforms popular dimensionality reduction methods such as the singular value decomposition (SVD) and non-negative matrix factorization (NMF) in a nearest neighbor prediction task. Moreover, our experimental results covering several language pairs such as English–French, English–Spanish, English–Greek, and English–Japanese show that the proposed method outperforms several other feature projection methods in biomedical term translation prediction tasks

An IR-based Approach Utilising Query Expansion for Plagiarism Detection in MEDLINE

The identification of duplicated and plagiarised passages of text has become an increasingly active area of research. In this paper we investigate methods for plagiarism detection that aim to identify potential sources of plagiarism from MEDLINE, particularly when the original text has been modified through the replacement of words or phrases. A scalable approach based on Information Retrieval is used to perform candidate document selection - the identification of a subset of potential source documents given a suspicious text - from MEDLINE. Query expansion is performed using the ULMS Metathesaurus to deal with situations in which original documents are obfuscated. Various approaches to Word Sense Disambiguation are investigated to deal with cases where there are multiple Concept Unique Identifiers (CUIs) for a given term. Results using the proposed IR-based approach outperform a state-of-the-art baseline based on Kullback-Leibler Distance