Neural Approaches to Relational Aspect-Based Sentiment Analysis. Exploring generalizations across words and languages

Jebbara S. Neural Approaches to Relational Aspect-Based Sentiment Analysis. Exploring generalizations across words and languages. Bielefeld: Universität Bielefeld; 2020.Everyday, vast amounts of unstructured, textual data are shared online in digital form. Websites such as forums, social media sites, review sites, blogs, and comment sections offer platforms to express and discuss opinions and experiences. Understanding the opinions in these resources is valuable for e.g. businesses to support market research and customer service but also individuals, who can benefit from the experiences and expertise of others. In this thesis, we approach the topic of opinion extraction and classification with neural network models. We regard this area of sentiment analysis as a relation extraction problem in which the sentiment of some opinion holder towards a certain aspect of a product, theme, or event needs to be extracted. In accordance with this framework, our main contributions are the following: 1. We propose a full system addressing all subtasks of relational sentiment analysis. 2. We investigate how semantic web resources can be leveraged in a neural-network-based model for the extraction of opinion targets and the classification of sentiment labels. Specifically, we experiment with enhancing pretrained word embeddings using the lexical resource WordNet. Furthermore, we enrich a purely text-based model with SenticNet concepts and observe an improvement for sentiment classification. 3. We examine how opinion targets can be automatically identified in noisy texts. Customer reviews, for instance, are prone to contain misspelled words and are difficult to process due to their domain-specific language. We integrate information about the character structure of a word into a sequence labeling system using character-level word embeddings and show their positive impact on the system's performance. We reveal encoded character patterns of the learned embeddings and give a nuanced view of the obtained performance differences. 4. Opinion target extraction usually relies on supervised learning approaches. We address the lack of available annotated data for specific languages by proposing a zero-shot cross-lingual approach for the extraction of opinion target expressions. We leverage multilingual word embeddings that share a common vector space across various languages and incorporate these into a convolutional neural network architecture. Our experiments with 5 languages give promising results: We can successfully train a model on annotated data of a source language and perform accurate prediction on a target language without ever using any annotated samples in that target language

Deep Memory Networks for Attitude Identification

We consider the task of identifying attitudes towards a given set of entities from text. Conventionally, this task is decomposed into two separate subtasks: target detection that identifies whether each entity is mentioned in the text, either explicitly or implicitly, and polarity classification that classifies the exact sentiment towards an identified entity (the target) into positive, negative, or neutral. Instead, we show that attitude identification can be solved with an end-to-end machine learning architecture, in which the two subtasks are interleaved by a deep memory network. In this way, signals produced in target detection provide clues for polarity classification, and reversely, the predicted polarity provides feedback to the identification of targets. Moreover, the treatments for the set of targets also influence each other -- the learned representations may share the same semantics for some targets but vary for others. The proposed deep memory network, the AttNet, outperforms methods that do not consider the interactions between the subtasks or those among the targets, including conventional machine learning methods and the state-of-the-art deep learning models.Comment: Accepted to WSDM'1

Comprehensive Review of Opinion Summarization

The abundance of opinions on the web has kindled the study of opinion summarization over the last few years. People have introduced various techniques and paradigms to solving this special task. This survey attempts to systematically investigate the different techniques and approaches used in opinion summarization. We provide a multi-perspective classification of the approaches used and highlight some of the key weaknesses of these approaches. This survey also covers evaluation techniques and data sets used in studying the opinion summarization problem. Finally, we provide insights into some of the challenges that are left to be addressed as this will help set the trend for future research in this area.unpublishednot peer reviewe

Basic tasks of sentiment analysis

Subjectivity detection is the task of identifying objective and subjective sentences. Objective sentences are those which do not exhibit any sentiment. So, it is desired for a sentiment analysis engine to find and separate the objective sentences for further analysis, e.g., polarity detection. In subjective sentences, opinions can often be expressed on one or multiple topics. Aspect extraction is a subtask of sentiment analysis that consists in identifying opinion targets in opinionated text, i.e., in detecting the specific aspects of a product or service the opinion holder is either praising or complaining about

A comparative analysis of recommender systems based on item aspect opinions extracted from user reviews

In popular applications such as e-commerce sites and social media, users provide online reviews giving personal opinions about a wide array of items, such as products, services and people. These reviews are usually in the form of free text, and represent a rich source of information about the users’ preferences. Among the information elements that can be extracted from reviews, opinions about particular item aspects (i.e., characteristics, attributes or components) have been shown to be effective for user modeling and personalized recommendation. In this paper, we investigate the aspect-based recommendation problem by separately addressing three tasks, namely identifying references to item aspects in user reviews, classifying the sentiment orientation of the opinions about such aspects in the reviews, and exploiting the extracted aspect opinion information to provide enhanced recommendations. Differently to previous work, we integrate and empirically evaluate several state-of-the-art and novel methods for each of the above tasks. We conduct extensive experiments on standard datasets and several domains, analyzing distinct recommendation quality metrics and characteristics of the datasets, domains and extracted aspects. As a result of our investigation, we not only derive conclusions about which combination of methods is most appropriate according to the above issues, but also provide a number of valuable resources for opinion mining and recommendation purposes, such as domain aspect vocabularies and domain-dependent, aspect-level lexiconsThis work was supported by the Spanish Ministry of Economy, Industry and Competitiveness (TIN2016-80630-P)