302 research outputs found
Comparing and Combining Sentiment Analysis Methods
Several messages express opinions about events, products, and services,
political views or even their author's emotional state and mood. Sentiment
analysis has been used in several applications including analysis of the
repercussions of events in social networks, analysis of opinions about products
and services, and simply to better understand aspects of social communication
in Online Social Networks (OSNs). There are multiple methods for measuring
sentiments, including lexical-based approaches and supervised machine learning
methods. Despite the wide use and popularity of some methods, it is unclear
which method is better for identifying the polarity (i.e., positive or
negative) of a message as the current literature does not provide a method of
comparison among existing methods. Such a comparison is crucial for
understanding the potential limitations, advantages, and disadvantages of
popular methods in analyzing the content of OSNs messages. Our study aims at
filling this gap by presenting comparisons of eight popular sentiment analysis
methods in terms of coverage (i.e., the fraction of messages whose sentiment is
identified) and agreement (i.e., the fraction of identified sentiments that are
in tune with ground truth). We develop a new method that combines existing
approaches, providing the best coverage results and competitive agreement. We
also present a free Web service called iFeel, which provides an open API for
accessing and comparing results across different sentiment methods for a given
text.Comment: Proceedings of the first ACM conference on Online social networks
(2013) 27-3
A model for providing emotion awareness and feedback using fuzzy logic in online learning
Monitoring users’ emotive states and using that information for providing feedback and scaffolding is crucial. In the learning context, emotions can be used to increase students’ attention as well as to improve memory and reasoning. In this context, tutors should be prepared to create affective learning situations and encourage collaborative knowledge construction as well as identify those students’ feelings which hinder learning process. In this paper, we propose a novel approach to label affective behavior in educational discourse based on fuzzy logic, which enables a human or virtual tutor to capture students’ emotions, make students aware of their own emotions, assess these emotions and provide appropriate affective feedback. To that end, we propose a fuzzy classifier that provides a priori qualitative assessment and fuzzy qualifiers bound to the amounts such as few, regular and many assigned by an affective dictionary to every word. The advantage of the statistical approach is to reduce the classical pollution problem of training and analyzing the scenario using the same dataset. Our approach has been tested in a real online learning environment and proved to have a very positive influence on students’ learning performance.Peer ReviewedPostprint (author's final draft
Sentiment Classification of Online Customer Reviews and Blogs Using Sentence-level Lexical Based Semantic Orientation Method
ABSTRACT
Sentiment analysis is the process of extracting knowledge from the peoples‟ opinions, appraisals and emotions toward entities, events and their attributes. These opinions
greatly impact on customers to ease their choices regarding online shopping, choosing events, products and entities. With the rapid growth of online resources, a vast amount
of new data in the form of customer reviews and opinions are being generated progressively. Hence, sentiment analysis methods are desirable for developing
efficient and effective analyses and classification of customer reviews, blogs and
comments.
The main inspiration for this thesis is to develop high performance domain
independent sentiment classification method. This study focuses on sentiment analysis
at the sentence level using lexical based method for different type data such as
reviews and blogs. The proposed method is based on general lexicons i.e. WordNet,
SentiWordNet and user defined lexical dictionaries for sentiment orientation. The
relations and glosses of these dictionaries provide solution to the domain portability problem. The experiments are performed on various data sets such as customer reviews and blogs comments. The results show that the proposed method with sentence contextual information is effective for sentiment classification. The proposed method performs better than word and text level corpus based machine learning methods for semantic orientation. The results highlight that the proposed method achieves an average accuracy of 86% at sentence-level and 97% at feedback level for customer reviews. Similarly, it achieves an average accuracy of 83% at sentence level and 86% at
feedback level for blog comment
Applying Deep Learning Techniques for Sentiment Analysis to Assess Sustainable Transport
Users voluntarily generate large amounts of textual content by expressing their opinions, in social media and specialized portals, on every possible issue, including transport and sustainability. In this work we have leveraged such User Generated Content to obtain a high accuracy sentiment analysis model which automatically analyses the negative and positive opinions expressed in the transport domain. In order to develop such model, we have semiautomatically generated an annotated corpus of opinions about transport, which has then been used to fine-tune a large pretrained language model based on recent deep learning techniques. Our empirical results demonstrate the robustness of our approach, which can be applied to automatically process massive amounts of opinions about transport. We believe that our method can help to complement data from official statistics and traditional surveys about transport sustainability. Finally, apart from the model and annotated dataset, we also provide a transport classification score with respect to the sustainability of the transport types found in the use case dataset.This work has been partially funded by the Spanish Ministry of Science, Innovation and Universities (DeepReading RTI2018-096846-B-C21, MCIU/AEI/FEDER, UE), Ayudas FundaciĂłn BBVA a Equipos de InvestigaciĂłn CientĂfica 2018 (BigKnowledge), DeepText (KK-2020/00088), funded by the Basque Government and the COLAB19/19 project funded by the UPV/EHU. Rodrigo Agerri is also funded by the RYC-2017-23647 fellowship and acknowledges the donation of a Titan V GPU by the NVIDIA Corporation
Building layered, multilingual sentiment lexicons at synset and lemma levels
Many tasks related to sentiment analysis rely on sentiment lexicons, lexical resources containing
information about the emotional implications of words (e.g., sentiment orientation of words, positive
or negative). In this work, we present an automatic method for building lemma-level sentiment lexicons,
which has been applied to obtain lexicons for English, Spanish and other three official languages in Spain.
Our lexicons are multi-layered, allowing applications to trade off between the amount of available words
and the accuracy of the estimations. Our evaluations show high accuracy values in all cases. As a previous
step to the lemma-level lexicons, we have built a synset-level lexicon for English similar to SENTIWORDNET
3.0, one of the most used sentiment lexicons nowadays. We have made several improvements in the
original SENTIWORDNET 3.0 building method, reflecting significantly better estimations of positivity and
negativity, according to our evaluations. The resource containing all the lexicons, ML-SENTICON, is publicly
available.Ministerio de EconomĂa y Competitividad TIN2012-38536-C03-0
Sentiment translation for low resourced languages: experiments on Irish general election Tweets
This paper presents two main methods of Sentiment Analysis
(SA) of User-Generated Content for a low-resource language: Irish. The
first method, automatic sentiment translation, applies existing English
SA resources to both manually- and automatically-translated tweets. We
obtained an accuracy of 70% using this approach. The second method involved the manual creation of an Irish-language sentiment lexicon: SentiFoclĂłir. This lexicon was used to build the first Irish SA system, SentiFocalTweet, which produced superior results to the first method, with
an accuracy of 76%. This demonstrates that translation from Irish to
English has a minor effect on the preservation of sentiment; it is also
shown that the SentiFocalTweet system is a successful baseline system
for Irish sentiment analysis
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