Exploiting Social Network Structure for Person-to-Person Sentiment Analysis

Person-to-person evaluations are prevalent in all kinds of discourse and important for establishing reputations, building social bonds, and shaping public opinion. Such evaluations can be analyzed separately using signed social networks and textual sentiment analysis, but this misses the rich interactions between language and social context. To capture such interactions, we develop a model that predicts individual A's opinion of individual B by synthesizing information from the signed social network in which A and B are embedded with sentiment analysis of the evaluative texts relating A to B. We prove that this problem is NP-hard but can be relaxed to an efficiently solvable hinge-loss Markov random field, and we show that this implementation outperforms text-only and network-only versions in two very different datasets involving community-level decision-making: the Wikipedia Requests for Adminship corpus and the Convote U.S. Congressional speech corpus

Exploiting and Ranking Dominating Product Features through Communal Sentiments

The rapidly expanding e-commerce has facilitated consumers to purchase products online. Various brands and millions of products have been offered online. Varieties of customers’ reviews are available now days in internet. These reviews are important for the consumers as well as the merchants. Most of the reviews are disorganized so it generates difficulty for usefulness of information. In this paper we are proposing a product feature ranking framework, which will identify important features of products from online customer opinions, and aim to improve the usability of the different reviews. The important product features are recognized using two observations 1) the important features are mostly commented on by a large number of users 2) users reviews on the important features are greatly influence on the overall reviews on the product. We first identify product features by shallow dependency parser and determine customer’s reviews on these features via a sentiment classifier. Then we adopt develop a probabilistic feature ranking algorithm to conclude the importance of features by considering frequency and the influence of the influence of the users reviews given to each feature over their overall reviews. DOI: 10.17762/ijritcc2321-8169.15068

Recommending Recommendations to Support the Defense Acquisition Workforce

Excerpt from the Proceedings of the Nineteenth Annual Acquisition Research SymposiumThis paper presentings the preliminary results of a research study to support the Defense Acquisition Workforce with a Natural Language Processing (NLP)/Machine Learning (ML) prototype of a system to determine what are the most relevant recommendations that stakeholders are providing to the Defense Acquisition community. The problem addressed by the research study is in the realm of NLP and ML and it is part of the quite popular category of “recommendation systems.” Unlike the majority of the cases in this category, though, this task does not focus on numerical data representing behaviors (like in shopping recommendations), but on extracting user-specific relevance from text and “recommending” a document or part of it. In order to identify important pieces of these texts, subjective text analysis is required to be run. The method used for the analysis is the “room theory framework” by Lipizzi et al. (2021) which applies the Framework Theory by Marvin Minsky (1974) through the use of text vectorization. This framework has three main components: a vectorized corpus representing the knowledge base of the specific domain (the “room”), a set of keywords or phrases defining the specific points of interest for the recommendation (the “benchmarks”) and the documents to be analyzed. The documents are then vectorized using the “room” and compared to the “benchmarks.” The sentences/paragraphs within a given document that are most similar to the benchmarks, and thus presumably the most important parts of the document, are highlighted. This enables the DAU reviewers to submit a document, run the program, and be able to clearly see what recommendations will be the most useful.Approved for public release; distribution is unlimited

Recommending Recommendations to Support the Defense Acquisition Workforce

Excerpt from the Proceedings of the Nineteenth Annual Acquisition Research SymposiumThis paper presentings the preliminary results of a research study to support the Defense Acquisition Workforce with a Natural Language Processing (NLP)/Machine Learning (ML) prototype of a system to determine what are the most relevant recommendations that stakeholders are providing to the Defense Acquisition community. The problem addressed by the research study is in the realm of NLP and ML and it is part of the quite popular category of “recommendation systems.” Unlike the majority of the cases in this category, though, this task does not focus on numerical data representing behaviors (like in shopping recommendations), but on extracting user-specific relevance from text and “recommending” a document or part of it. In order to identify important pieces of these texts, subjective text analysis is required to be run. The method used for the analysis is the “room theory framework” by Lipizzi et al. (2021) which applies the Framework Theory by Marvin Minsky (1974) through the use of text vectorization. This framework has three main components: a vectorized corpus representing the knowledge base of the specific domain (the “room”), a set of keywords or phrases defining the specific points of interest for the recommendation (the “benchmarks”) and the documents to be analyzed. The documents are then vectorized using the “room” and compared to the “benchmarks.” The sentences/paragraphs within a given document that are most similar to the benchmarks, and thus presumably the most important parts of the document, are highlighted. This enables the DAU reviewers to submit a document, run the program, and be able to clearly see what recommendations will be the most useful.Approved for public release; distribution is unlimited

Sentiment Analysis over Online Product Reviews: A Survey

Prior to the invention of the internet while purchasing any product people used to ask the opinions to his family, friends for particular product. but now a days as the swift increase of usage of the internet, more users are motivated to write their feelings about particulars in the form of comments on different sites like Facebook, twitter, online shopping sites, blogs, etc. this comments are nothing but the sentiments of the users this may be positive, negative or neutral. There are various techniques used for summarizing the customer comments like Data mining, Text clssification, Retrieval of informtaion, and summarizing the text. People tend to write their reviews over a product over different sites. Most of the reviews are critical to conclude so it generates difficulty for usefulness of information. If anyone want to know the impact of the particular post/product then it becomes difficult to read all the comments and to classify it. Sentiment analysis is the ongoing research field in the data mining, Sentiment analysis is also referred as opinion mining. This field mainly deals with classifying the sentiments among different types of comments that are written by various users. This paper is about to discuss different techniques, challenges and applications related to sentiment analysis

Says who? Automatic text-based content analysis of television news

ABSTRACT We perform an automatic analysis of television news programs, based on the closed captions that accompany them. Specifically, we collect all the news broadcasted in over 140 television channels in the US during a period of six months. We start by segmenting, processing, and annotating the closed captions automatically. Next, we focus on the analysis of their linguistic style and on mentions of people using NLP methods. We present a series of key insights about news providers, people in the news, and we discuss the biases that can be uncovered by automatic means. These insights are contrasted by looking at the data from multiple points of view, including qualitative assessment