Harvesting and Summarizing User-Generated Content for Advanced Speech-Based HCI

Abstract

Abstract-There are many Web-based platforms where people could share user-generated content such as reviews, posts, blogs, and tweets. However, online communities and social networks are expanding so rapidly that it is impossible for people to digest all the information. To help users obtain information more efficiently, both the interface for data access and the information representation need to be improved. An intuitive and personalized interface, such as a dialogue system, could be an ideal assistant, which engages a user in a continuous dialogue to garner the user's interest, assists the user via speech-navigated interactions, harvests and summarizes the Web data as well as presenting it in a natural way. This work, therefore, aims to conduct research on a universal framework for developing a speech-based interface that can aggregate user-generated content and present the summarized information via speech-based human-computer interactions. The challenge is two-fold. Firstly, how to interpret the semantics and sentiment of user-generated data and aggregate them into structured yet concise summaries? Secondly, how to develop a dialogue modeling mechanism to present the highlighted information via natural language? This work explores plausible approaches to tackling these challenges. We will investigate a parse-and-paraphrase paradigm and a sentiment scoring mechanism for information extraction from unstructured user-generated content. We will also explore sentiment-involved opinion summarization and dialogue modeling approaches for aggregated information representation. A restaurant-domain prototype system has been implemented for demonstration.Index Terms-Spoken dialogue systems, user-generated content processing.

Summarizing Online Movie Reviews: A Machine Learning Approach to Big Data Analytics

“…ere are various approaches that classify review text into negative and positive reviews such as dictionarybased approaches and machine learning (ML) approaches. Various ML-based approaches such as support vector machine (SVM) [5], decision trees [6], and neural networks [7] have been presented for text classification and revealed their abilities in various domains. NB is a state-of-the-art ML algorithm and has been proved to be very effective in traditional text classification.…”

“…Review summarization is a procedure in which a summary is generated from a gigantic amount of review sentences [10]. Numerous techniques such as supervised ML based [5,6] and unsupervised/lexicon based [10,11] have been applied for review summarization. However, the unsupervised/lexicon-based approaches heavily rely on linguistic resources and are limited to words present in the lexicon.…”

Opinion Mining and Summarization of Hotel Reviews

“…Many researchers performed sentiment analysis on different domains data such as movie [4], product [13], and social network [3] [15]. Many researchers also worked on summarization of reviews [1] [6] [7]. In a survey of Pang and Lee [9] they presented concept of opinion mining, its application and challenges involved it.…”

“…Machine learning method uses different classification algorithms for opinion classification. Classification algorithms such as Naive Bayes [11], SVM [4], and Decision Tree [1] work well for review text. Other methods such as resource based opinion mining [6], [7] also proposed by different researchers.…”

“…Authors also proposed aspect based summarization of product reviews in [11] [12]. In [1] author presented aspects and related word phrases as summary. Many researchers used Latent semantic analysis (LSA) for opinion summarization of user reviews [4] [7].…”