A literature survey of methods for analysis of subjective language

Subjective language is used to express attitudes and opinions towards things, ideas and people. While content and topic centred natural language processing is now part of everyday life, analysis of subjective aspects of natural language have until recently been largely neglected by the research community. The explosive growth of personal blogs, consumer opinion sites and social network applications in the last years, have however created increased interest in subjective language analysis. This paper provides an overview of recent research conducted in the area

Determining the polarity of postings for discussion search

When performing discussion search it might be desirable to consider non-topical measures like the number of positive and negative replies to a posting, for instance as one possible indicator for the trustworthiness of a comment. Systems like POLAR are able to integrate such values into the retrieval function. To automatically detect the polarity of postings, they need to be classified into positive and negative ones w.r.t.\ the comment or document they are annotating. We present a machine learning approach for polarity detection which is based on Support Vector Machines. We discuss and identify appropriate term and context features. Experiments with ZDNet News show that an accuracy of around 79\%-80\% can be achieved for automatically classifying comments according to their polarity

Improving Care using Network-Based Modeling and Intelligent Data Mining of Social Media

Cleverly extracting information from social media has recently attracted nice interest from the medication and Health science community to at an identical time improve health care outcomes and deflate prices victimization consumer-generated opinion. We've got an inclination to tend to propose a social dancing analysis framework that focuses on positive and negative sentiment, in addition as a result of the aspect effects of treatment, in users’ forum posts, and identi?es user communities (modules) and in?uential users for the aim of ascertaining user opinion of cancer treatment. We get a preference to tend to use a self-organizing map to investigate word frequency information derived from users’ forum posts. we've got an inclination to tend to then introduced a unique network-based approach for modeling users’ forum interactions and utilized a network partitioning technique supported optimizing a stability quality live. This allowed North American nation to work out shopper opinion and establish in?uential users at intervals the retrieved modules victimization data derived from each word-frequency information and network-based properties. Our approach will expand analysis into showing intelligence mining social media information for shopper opinion of assorted treatments to supply fast, up-to-date data for the pharmaceutical trade, hospitals, and medical employees, on the effectiveness (or ineffectiveness) of future treatments

Visualization of Online Deals

This project identifies the impact of online deals and coupons on the life of people. The basic idea is to find the trend in the sales of these deals and this project would be helpful for companies, restaurants and dealers who are trying to sell coupons to popularize their product. The final output of the project would be a timeline graph with the deals displayed based on the month of their sale. Information like the original price, deal price, discount and number of coupons sold will be displayed in a pop-up window when a deal is selected. Different colors are used to differentiate the different types of deals. A pie chart depicting this information for various US states is created which provides a summarized view of the deals. This way, an overall picture about the deals sold for a particular month can be obtained

Mining Online Deal Forums for Hot Deals

Online deal forums are public places where participants share with each other news and information regarding “deals ” such as sales promotion events by online stores. The large number of messages in the forums and their inherent uncertainty make it difficult for even seasoned users to identify useful deal information from the forums. We develop an intelligent deal alert service which assists ordinary Web surfers to find useful deals by mining online deal forums. It periodically crawls relevant deal forums to collect fresh message posts and responses, and evaluate them using a form of probabilistic text classification. Users may be notified of new, “potentially ” useful deal messages via emails or they may browse them using their favorite Web browser. We train and evaluate the service using deal posts and responses collected from actual deal forums in the Web. The preliminary evaluation results show that the service is quite effective in reducing the time to find useful deals. 1

Predicting Linguistic Structure with Incomplete and Cross-Lingual Supervision

Contemporary approaches to natural language processing are predominantly based on statistical machine learning from large amounts of text, which has been manually annotated with the linguistic structure of interest. However, such complete supervision is currently only available for the world's major languages, in a limited number of domains and for a limited range of tasks. As an alternative, this dissertation considers methods for linguistic structure prediction that can make use of incomplete and cross-lingual supervision, with the prospect of making linguistic processing tools more widely available at a lower cost. An overarching theme of this work is the use of structured discriminative latent variable models for learning with indirect and ambiguous supervision; as instantiated, these models admit rich model features while retaining efficient learning and inference properties. The first contribution to this end is a latent-variable model for fine-grained sentiment analysis with coarse-grained indirect supervision. The second is a model for cross-lingual word-cluster induction and the application thereof to cross-lingual model transfer. The third is a method for adapting multi-source discriminative cross-lingual transfer models to target languages, by means of typologically informed selective parameter sharing. The fourth is an ambiguity-aware self- and ensemble-training algorithm, which is applied to target language adaptation and relexicalization of delexicalized cross-lingual transfer parsers. The fifth is a set of sequence-labeling models that combine constraints at the level of tokens and types, and an instantiation of these models for part-of-speech tagging with incomplete cross-lingual and crowdsourced supervision. In addition to these contributions, comprehensive overviews are provided of structured prediction with no or incomplete supervision, as well as of learning in the multilingual and cross-lingual settings. Through careful empirical evaluation, it is established that the proposed methods can be used to create substantially more accurate tools for linguistic processing, compared to both unsupervised methods and to recently proposed cross-lingual methods. The empirical support for this claim is particularly strong in the latter case; our models for syntactic dependency parsing and part-of-speech tagging achieve the hitherto best published results for a wide number of target languages, in the setting where no annotated training data is available in the target language