92,741 research outputs found
Domain identification through sentiment analysis
When dealing with chatbots, domain identification is an important feature to adapt the interactions between user and computer in order to increase the reliability of the communication and, consequently, the audience and decrease its rejection avoiding misunderstandings. In order to adapt to different domains, the writing style will be different for the same author. For example, the same person in the role of a student writes to his professor in a different style than he does for his brother. This article presents a process that uses sentiment analysis to identify the average emotional profile of the communication scenario where the conversation is done. Using Natural Language Processing and Machine Learning techniques, it was possible to obtain an index of 96.21% of correct classifications in the identification of where these communications have occurred only analysing the emotional profile of these texts.This work has been supported by COMPETE: POCI-01-0145-FEDER-0070 43and FCT – Fundação para a Ciência e Tecnologia within the Project Scope: UID/CEC/ 00319/201
Sentiment detection in social networks and in collaborative learning environments
Daily millions of messages appear on the web, which is becoming a rich source of data for opinion mining and sentiment analysis. The computational study of opinions, feelings and emotions expressed in a text often relates to the identification of agreement or disagreement with statements, contained in comments or reviews, that convey positive or negative feelings. The detection and analysis of sentiment in textual communication is a topic attracting attention also in the context of collaborative learning in social networks, being learners actively engaged in presenting and defending ideas and opinions, as well as exchanging moods about courses with peers. In this paper, we investigate the adoption of a probabilistic approach based on the Latent Dirichlet Allocation (LDA) as Sentiment Grabber. Through this approach, for a set of documents belonging to a same knowledge domain, a graph, the Mixed Graph of Terms, can be automatically extracted. The paper shows how this graph contains a set of weighted word pairs, which are discriminative for sentiment classification. The proposed method has been tested in different context: a standard dataset containing movie reviews; a real-time analysis of social networks posts; a collaborative learning scenario. The experimental evaluation shows how the proposed approach is effective and satisfactory
Lingmotif: una Herramienta de Análisis de Sentimiento Enfocada en el Usuario
In this paper, we describe Lingmotif, a lexicon-based, linguistically-motivated, user-friendly, GUI-enabled, multi-platform, Sentiment Analysis desktop application. Lingmotif can perform SA on any type of input texts, regardless of their length and topic. The analysis is based on the identification of sentiment-laden words and phrases contained in the application's rich core lexicons, and employs context rules to account for sentiment shifters. It offers easy-to-interpret visual representations of quantitative data, as well as a detailed, qualitative analysis of the text in terms of its sentiment. Lingmotif can also take user-provided plugin lexicons in order to account for domain-specific sentiment expression. As of version 1.0, Lingmotif analyzes English and Spanish texts. Lingmotif thus aims to become a general-purpose Sentiment Analysis tool for discourse analysis, rhetoric, psychology, marketing, the language industries, and others.En este artÃculo se describe Lingmotif, una aplicación de Análisis de Sentimiento multi-plataforma, con interfaz gráfica de usuario amigable, motivada lingüÃsticamente y basada en léxico. Lingmotif efectúa Análisis de Sentimiento sobre cualquier tipo de texto, independientemente de su tamaño o tema. El análisis se basa en la identificación en el texto de palabras y frases con carga afectiva, contenidas en los diccionarios de la aplicación, y aplica reglas de contexto para dar cabida a modificadores del sentimiento. Ofrece representaciones gráficas fáciles de interpretar de los datos cuantitativos, asà como un análisis detallado del texto. Lingmotif también puede utilizar léxicos del usuario a modo de plugins, de tal modo que es posible analizar de forma efectiva la expresión del sentimiento en dominios especÃficos. La versión 1.0 de Lingmotif está preparada para trabajar con textos en español e inglés. De este modo, se conforma como una herramienta de propósito general en el ámbito del Análisis de Sentimiento para el análisis del discurso, retórica, psicologÃa, marketing, las industrias de la lengua y otras.This research was supported by Spain’s MINECO through the funding of project Lingmotif2 (FFI2016-78141-P)
Transductive Learning with String Kernels for Cross-Domain Text Classification
For many text classification tasks, there is a major problem posed by the
lack of labeled data in a target domain. Although classifiers for a target
domain can be trained on labeled text data from a related source domain, the
accuracy of such classifiers is usually lower in the cross-domain setting.
Recently, string kernels have obtained state-of-the-art results in various text
classification tasks such as native language identification or automatic essay
scoring. Moreover, classifiers based on string kernels have been found to be
robust to the distribution gap between different domains. In this paper, we
formally describe an algorithm composed of two simple yet effective
transductive learning approaches to further improve the results of string
kernels in cross-domain settings. By adapting string kernels to the test set
without using the ground-truth test labels, we report significantly better
accuracy rates in cross-domain English polarity classification.Comment: Accepted at ICONIP 2018. arXiv admin note: substantial text overlap
with arXiv:1808.0840
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OBOME - Ontology based opinion mining in UBIPOL
Ontologies have a special role in the UBIPOL system, they help to structure the policy related context, provide conceptualization for policy domain and use in the opinion mining process. In this work we presented a system called Ontology Based Opinion Mining Engine (OBOME) for analyzing a domain-specific opinion corpus by first assisting the user with the creation of a domain ontology from the corpus. We determined the polarity of opinion on the various domain aspects. In the former step, the policy domain aspect has are identified (namely which policy category is represented by the concept). This identification is supported by the policy modelling ontology, which describe the most important policy – related classes and structure. Then the most informative documents from the corpus are extracted and asked the user to create a set of aspects and related keywords using these documents. In the latter step, we used the corpus specific ontology to model the domain and extracted aspect-polarity associations using grammatical dependencies between words. Later, summarized results are shown to the user to analyze and store. Finally, in an offline process policy modeling ontology is updated
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
Models of Social Groups in Blogosphere Based on Information about Comment Addressees and Sentiments
This work concerns the analysis of number, sizes and other characteristics of
groups identified in the blogosphere using a set of models identifying social
relations. These models differ regarding identification of social relations,
influenced by methods of classifying the addressee of the comments (they are
either the post author or the author of a comment on which this comment is
directly addressing) and by a sentiment calculated for comments considering the
statistics of words present and connotation. The state of a selected blog
portal was analyzed in sequential, partly overlapping time intervals. Groups in
each interval were identified using a version of the CPM algorithm, on the
basis of them, stable groups, existing for at least a minimal assumed duration
of time, were identified.Comment: Gliwa B., Ko\'zlak J., Zygmunt A., Models of Social Groups in
Blogosphere Based on Information about Comment Addressees and Sentiments, in
the K. Aberer et al. (Eds.): SocInfo 2012, LNCS 7710, pp. 475-488, Best Paper
Awar
Task-specific Word Identification from Short Texts Using a Convolutional Neural Network
Task-specific word identification aims to choose the task-related words that
best describe a short text. Existing approaches require well-defined seed words
or lexical dictionaries (e.g., WordNet), which are often unavailable for many
applications such as social discrimination detection and fake review detection.
However, we often have a set of labeled short texts where each short text has a
task-related class label, e.g., discriminatory or non-discriminatory, specified
by users or learned by classification algorithms. In this paper, we focus on
identifying task-specific words and phrases from short texts by exploiting
their class labels rather than using seed words or lexical dictionaries. We
consider the task-specific word and phrase identification as feature learning.
We train a convolutional neural network over a set of labeled texts and use
score vectors to localize the task-specific words and phrases. Experimental
results on sentiment word identification show that our approach significantly
outperforms existing methods. We further conduct two case studies to show the
effectiveness of our approach. One case study on a crawled tweets dataset
demonstrates that our approach can successfully capture the
discrimination-related words/phrases. The other case study on fake review
detection shows that our approach can identify the fake-review words/phrases.Comment: accepted by Intelligent Data Analysis, an International Journa
Discovering a tourism destination with social media data: BERT-based sentiment analysis
Purpose – The main purpose of this paper is to analyze a tourist destination using sentiment analysis
techniques with data from Twitter and Instagram to find the most representative entities (or places) and
perceptions (or aspects) of the users.
Design/methodology/approach – The authors used 90,725 Instagram posts and 235,755 Twitter tweets
to analyze tourism in Granada (Spain) to identify the important places and perceptions mentioned by travelers
on both social media sites. The authors used several approaches for sentiment classification for English and
Spanish texts, including deep learning models.
Findings – The best results in a test set were obtained using a bidirectional encoder representations
from transformers (BERT) model for Spanish texts and Tweeteval for English texts, and these were
subsequently used to analyze the data sets. It was then possible to identify the most important
entities and aspects, and this, in turn, provided interesting insights for researchers, practitioners,
travelers and tourism managers so that services could be improved and better marketing strategies
formulated.
Research limitations/implications – The authors propose a Spanish-Tourism-BERT model for
performing sentiment classification together with a process to find places through hashtags and to reveal the
important negative aspects of each place.
Practical implications – The study enables managers and practitioners to implement the Spanish-BERT
model with our Spanish Tourism data set that the authors released for adoption in applications to find both
positive and negative perceptions.
Originality/value – This study presents a novel approach on how to apply sentiment analysis in
the tourism domain. First, the way to evaluate the different existing models and tools is presented;
second, a model is trained using BERT (deep learning model); third, an approach of how to identify
the acceptance of the places of a destination through hashtags is presented and, finally, the
evaluation of why the users express positivity (negativity) through the identification of entities and
aspects.Spanish Ministerio de Ciencia e Innovacion, Agencia Estatal de Investigacion PID2019-106758GB-C31European Commissio
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