User generated content is extremely valuable for mining market intelligence because it is unsolicited. We study the problem of analyzing users' sentiment and opinion in their blog, message board, etc. posts with respect to topics expressed as a search query. In the scenario we consider the matches of the search query terms are expanded through coreference and meronymy to produce a set of mentions. The mentions are contextually evaluated for sentiment and their scores are aggregated (using a data structure we introduce call the sentiment propagation graph) to produce an aggregate score for the input entity. An extremely crucial part in the contextual evaluation of individual mentions is finding which sentiment expressions are semantically related to (target) which mentions --- this is the focus of our paper. We present an approach where potential target mentions for a sentiment expression are ranked using supervised machine learning (Support Vector Machines) where the main features are the syntactic configurations (typed dependency paths) connecting the sentiment expression and the mention. We have created a large English corpus of product discussions blogs annotated with semantic types of mentions, coreference, meronymy and sentiment targets. The corpus proves that coreference and meronymy are not marginal phenomena but are really central to determining the overall sentiment for the top-level entity. We evaluate a number of techniques for sentiment targeting and present results which we believe push the current state-of-the-art.
Machine learning clustering techniques are not the only way to extract topics from a text data set. Text mining literature has proposed a number of statistical models, known as probabilistic topic models, to detect topics from an unlabeled set of documents. One of the most popular models is the latent Dirichlet allocation (LDA) algorithm developed by Blei, Ng, and Jordan [i]. LDA is a generative unsupervised probabilistic algorithm that isolates the top K topics in a data set as described by the most relevant N keywords. In other words, the documents in the data set are represented as random mixtures of latent topics, where each topic is characterized by a Dirichlet distribution over a fixed vocabulary.
George, Clint Pazhayidam (University of Florida) | Puri, Sahil (University of Florida) | Wang, Daisy Zhe (University of Florida) | Wilson, Joseph N. (University of Florida) | Hamilton, William F. (University of Florida)
Electronic discovery is an interesting subproblem of information retrieval in which one identifies documents that are potentially relevant to issues and facts of a legal case from an electronically stored document collection (a corpus). In this paper, we consider representing documents in a topic space using the well-known topic models such as latent Dirichlet allocation and latent semantic indexing, and solving the information retrieval problem via finding document similarities in the topic space rather doing it in the corpus vocabulary space. We also develop an iterative SMART ranking and categorization framework including human-in-the-loop to label a set of seed (training) documents and using them to build a semi-supervised binary document classification model based on Support Vector Machines. To improve this model, we propose a method for choosing seed documents from the whole population via an active learning strategy. We report the results of our experiments on a real dataset in the electronic discovery domain.
Natural Language Processing (NLP) is a subfield of machine learning concerned with processing and analyzing natural language data, usually in the form of text or audio. Some common challenges within NLP include speech recognition, text generation, and sentiment analysis, while some high-profile products deploying NLP models include Apple's Siri, Amazon's Alexa, and many of the chatbots one might interact with online. To get started with NLP and introduce some of the core concepts in the field, we're going to build a model that tries to predict the sentiment (positive, neutral, or negative) of tweets relating to US Airlines, using the popular Twitter US Airline Sentiment dataset. Code snippets will be included in this post, but for fully reproducible notebooks and scripts, view all of the notebooks and scripts associated with this project on its Comet project page. Let's start by importing some libraries.
Political discourse in the United States is getting increasingly polarized. This polarization frequently causes different communities to react very differently to the same news events. Political blogs as a form of social media provide an unique insight into this phenomenon. We present a multitarget, semisupervised latent variable model, MCR-LDA to model this process by analyzing political blogs posts and their comment sections from different political communities jointly to predict the degree of polarization that news topics cause. Inspecting the model after inference reveals topics and the degree to which it triggers polarization. In this approach, community responses to news topics are observed using sentiment polarity and comment volume which serves as a proxy for the level of interest in the topic. In this context, we also present computational methods to assign sentiment polarity to the comments which serve as targets for latent variable models that predict the polarity based on the topics in the blog content. Our results show that the joint modeling of communities with different political beliefs using MCR-LDA does not sacrifice accuracy in sentiment polarity prediction when compared to approaches that are tailored to specific communities and additionally provides a view of the polarization in responses from the different communities.