119 research outputs found
SentiTur: Building Linguistic Resources for Aspect-Based Sentiment Analysis in the Tourism Sector
The use of linguistic resources beyond the scope of language studies, i.e. commercial purposes, has become commonplace since the availability of massive amounts of data and the development of tools to process them. An interesting focus on these materials is provided by Sentiment Analysis (SA) tools and methodologies, which attempt to identify the polarity or semantic orientation of a text, i.e., its positive, negative, or neutral value. Two main approaches have been made in this sense, one based on complex machine-learning algorithms and the other relying principally on lexical knowledge (Taboada et al., 2011). Lingmotif is an example of lexicon-based SA tool offering polarity classification and other related metrics, together with an analysis of the target segments evaluated (Moreno-Ortiz, 2017). Sentiment has been shown to be domain-specific to a large extent (Choi & Cardie, 2008) and it is therefore necessary to study and describe how sentiment is expressed not only in general language, but also in specialized domains. The availability of annotated, domain-specific corpora could greatly enhance the capacity of SA tools.
Furthermore, the demand for a more fine-grained approach requires the identification of specific domain terminology, allowing the recognition of target terms associated with the polarity (Liu, 2012). Most available SA corpora are annotated at the document level, which allows systems to be trained to return the overall orientation of the text. However, more detail is necessary: what aspects exactly are being praised or criticized? This type SA is known as Aspect-Based Sentiment Analysis (ABSA), and attempts to extract more fined-grained knowledge. ABSA has attracted the attention of recent SemEval shared-tasks (Pontiki et al., 2015)
Method for Aspect-Based Sentiment Annotation Using Rhetorical Analysis
This paper fills a gap in aspect-based sentiment analysis and aims to present
a new method for preparing and analysing texts concerning opinion and
generating user-friendly descriptive reports in natural language. We present a
comprehensive set of techniques derived from Rhetorical Structure Theory and
sentiment analysis to extract aspects from textual opinions and then build an
abstractive summary of a set of opinions. Moreover, we propose aspect-aspect
graphs to evaluate the importance of aspects and to filter out unimportant ones
from the summary. Additionally, the paper presents a prototype solution of data
flow with interesting and valuable results. The proposed method's results
proved the high accuracy of aspect detection when applied to the gold standard
dataset
Large-Scale Goodness Polarity Lexicons for Community Question Answering
We transfer a key idea from the field of sentiment analysis to a new domain:
community question answering (cQA). The cQA task we are interested in is the
following: given a question and a thread of comments, we want to re-rank the
comments so that the ones that are good answers to the question would be ranked
higher than the bad ones. We notice that good vs. bad comments use specific
vocabulary and that one can often predict the goodness/badness of a comment
even ignoring the question, based on the comment contents only. This leads us
to the idea to build a good/bad polarity lexicon as an analogy to the
positive/negative sentiment polarity lexicons, commonly used in sentiment
analysis. In particular, we use pointwise mutual information in order to build
large-scale goodness polarity lexicons in a semi-supervised manner starting
with a small number of initial seeds. The evaluation results show an
improvement of 0.7 MAP points absolute over a very strong baseline and
state-of-the art performance on SemEval-2016 Task 3.Comment: SIGIR '17, August 07-11, 2017, Shinjuku, Tokyo, Japan; Community
Question Answering; Goodness polarity lexicons; Sentiment Analysi
Rude waiter but mouthwatering pastries! An exploratory study into Dutch aspect-based sentiment analysis
The fine-grained task of automatically detecting all sentiment expressions within a given document and the aspects to which they refer is known as aspect-based sentiment analysis. In this paper we present the first full aspect-based sentiment analysis pipeline for Dutch
and apply it to customer reviews. To this purpose, we collected reviews from two different domains, i.e. restaurant and smartphone reviews. Both corpora have been manually annotated using newly developed guidelines that comply to standard practices in the field. For our experimental pipeline we perceive aspect-based sentiment analysis as a task consisting of three main subtasks which have to be tackled incrementally: aspect term extraction, aspect category classification and polarity classification. First experiments on our Dutch restaurant corpus reveal that this is indeed a feasible approach that yields promising results
Aspect-Based Sentiment Analysis Using a Two-Step Neural Network Architecture
The World Wide Web holds a wealth of information in the form of unstructured
texts such as customer reviews for products, events and more. By extracting and
analyzing the expressed opinions in customer reviews in a fine-grained way,
valuable opportunities and insights for customers and businesses can be gained.
We propose a neural network based system to address the task of Aspect-Based
Sentiment Analysis to compete in Task 2 of the ESWC-2016 Challenge on Semantic
Sentiment Analysis. Our proposed architecture divides the task in two subtasks:
aspect term extraction and aspect-specific sentiment extraction. This approach
is flexible in that it allows to address each subtask independently. As a first
step, a recurrent neural network is used to extract aspects from a text by
framing the problem as a sequence labeling task. In a second step, a recurrent
network processes each extracted aspect with respect to its context and
predicts a sentiment label. The system uses pretrained semantic word embedding
features which we experimentally enhance with semantic knowledge extracted from
WordNet. Further features extracted from SenticNet prove to be beneficial for
the extraction of sentiment labels. As the best performing system in its
category, our proposed system proves to be an effective approach for the
Aspect-Based Sentiment Analysis
A Unified Model for Opinion Target Extraction and Target Sentiment Prediction
Target-based sentiment analysis involves opinion target extraction and target
sentiment classification. However, most of the existing works usually studied
one of these two sub-tasks alone, which hinders their practical use. This paper
aims to solve the complete task of target-based sentiment analysis in an
end-to-end fashion, and presents a novel unified model which applies a unified
tagging scheme. Our framework involves two stacked recurrent neural networks:
The upper one predicts the unified tags to produce the final output results of
the primary target-based sentiment analysis; The lower one performs an
auxiliary target boundary prediction aiming at guiding the upper network to
improve the performance of the primary task. To explore the inter-task
dependency, we propose to explicitly model the constrained transitions from
target boundaries to target sentiment polarities. We also propose to maintain
the sentiment consistency within an opinion target via a gate mechanism which
models the relation between the features for the current word and the previous
word. We conduct extensive experiments on three benchmark datasets and our
framework achieves consistently superior results.Comment: AAAI 201
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