scholarly journals Learning rules for automatic identification of implicit aspects in Portuguese

2021 ◽  
Author(s):  
Mateus Tarcinalli Machado ◽  
Thiago Alexandre Salgueiro Pardo ◽  
Evandro Eduardo Seron Ruiz ◽  
Ariani Di Felippo
Keyword(s):  
Sentiment Analysis ◽  
English Language ◽  
Corpus Analysis ◽  
Automatic Identification ◽  
Learning Rules

This sentiment analysis work is focused on the task of identifying aspects, emphasizing the so-called implicit aspects, i.e., those that are not explicitly mentioned in the texts. For this, we analyzed frequency-based methods, adapted rules from the English language to Portuguese, and developed a method that learns new rules through corpus analysis.

An historical analysis of species references in American English

Corpora ◽  
2019 ◽  
Vol 14 (3) ◽  
pp. 327-349
Author(s):  
Craig Frayne
Keyword(s):  
Environmental Change ◽  
Sentiment Analysis ◽  
Quantitative Methods ◽  
English Language ◽  
Language Use ◽  
American English ◽  
Historical Analysis ◽  
The Past ◽  
Corpus Studies ◽  
Google Books

This study uses the two largest available American English language corpora, Google Books and the Corpus of Historical American English (coha), to investigate relations between ecology and language. The paper introduces ecolinguistics as a promising theme for corpus research. While some previous ecolinguistic research has used corpus approaches, there is a case to be made for quantitative methods that draw on larger datasets. Building on other corpus studies that have made connections between language use and environmental change, this paper investigates whether linguistic references to other species have changed in the past two centuries and, if so, how. The methodology consists of two main parts: an examination of the frequency of common names of species followed by aspect-level sentiment analysis of concordance lines. Results point to both opportunities and challenges associated with applying corpus methods to ecolinguistc research.

scholarly journals A comparative review of the challenges encountered in sentiment analysis of Indian regional language tweets vs English language tweets

2018 ◽  
Vol 7 (2.21) ◽  
pp. 319
Author(s):  
Saini Jacob Soman ◽  
P Swaminathan ◽  
R Anandan ◽  
K Kalaivani
Keyword(s):  
Sentiment Analysis ◽  
Life Events ◽  
Social Networking Sites ◽  
Positive Emotions ◽  
English Language ◽  
Indian Languages ◽  
Regional Language ◽  
Comparative Review ◽  
Computational Research ◽  
Regional Languages

With the developed use of online medium these days for sharing views, sentiments and opinions about products, services, organization and people, micro blogging and social networking sites are acquiring a huge popularity. One of the biggest social media sites namely Twitter is used by several people to share their life events, views and opinion about different areas and concepts. Sentiment analysis is the computational research of reviews, opinions, attitudes, views and peoples’ emotions about different products, services, firms and topics through categorizing them as negative and positive emotions. Sentiment analysis of tweets is a challenging task. This paper makes a critical review on the comparison of the challenges associated with sentiment analysis of Tweets in English Language versus Indian Regional Languages. Five Indian languages namely Tamil, Malayalam, Telugu, Hindi and Bengali have been considered in this research and several challenges associated with the analysis of Twitter sentiments in those languages have been identified and conceptualized in the form of a framework in this research through systematic review.  

scholarly journals A Check on Annotation in Sentiment Research

2019 ◽  
Vol 8 (2S8) ◽  
pp. 1346-1350
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
English Language ◽  
Research Area ◽  
Research Literature ◽  
Manual Annotation ◽  
Automatic Annotation ◽  
Annotation Method ◽  
New Concepts

The research literature on sentiment analysis methodologies has exponentially grown in recent years. In any research area, where new concepts and techniques are constantly introduced, it is, therefore, of interest to analyze the latest trends in this literature. In particular, we have chosen to primarily focus on the literature of the last five years, on annotation methodologies, including frequently used datasets and from which they were obtained. Based on the survey, it appears that researchers do more manual annotation in the formation of sentiment corpus. As for the dataset, there are still many uses of English language taken from social media such as Twitter. In this area of research, there are still many that need to be explored, such as the use of semi-automatic annotation method that is still very rarely used by researchers. Also, less popular languages, such as Malay, Korean, Japanese, and so on, still require corpus for sentiment analysis research.

Research into practice: The influence of discourse studies on language descriptions and task design in published ELT materials

2015 ◽  
Vol 48 (4) ◽  
pp. 506-530 ◽  
Author(s):  
Alex Gilmore
Keyword(s):  
Discourse Analysis ◽  
Language Learning ◽  
Critical Discourse Analysis ◽  
Conversation Analysis ◽  
English Language ◽  
Language Teachers ◽  
Corpus Analysis ◽  
English Language Teaching ◽  
Genre Analysis ◽  
Discourse Studies

Discourse studies is a vast, multidisciplinary, and rapidly expanding area of research, embracing a range of approaches including discourse analysis, corpus analysis, conversation analysis, interactional sociolinguistics, critical discourse analysis, genre analysis and multimodal discourse analysis. Each approach offers its own unique perspective on discourse, focusing variably on text, context or a range of semiotic modes. Together, they provide foreign language teachers and material designers with new insights into language, and are beginning to have an observable impact on published English Language Teaching (ELT) materials. This paper examines the ways in which the four approaches with the strongest links to the ELT profession (corpus analysis, conversation analysis, discourse analysis and genre analysis) have found their way into language learning materials, and offers some suggestions on how discourse studies may influence ELT classrooms in the future.

scholarly journals MNCN: A Multilingual Ngram-Based Convolutional Network for Aspect Category Detection in Online Reviews

2019 ◽  
Vol 33 ◽  
pp. 6441-6448 ◽  
Author(s):  
Erfan Ghadery ◽  
Sajad Movahedi ◽  
Heshaam Faili ◽  
Azadeh Shakery
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Sentiment Analysis ◽  
English Language ◽  
Online Reviews ◽  
The Internet ◽  
Word Embeddings ◽  
Convolutional Network ◽  
Significant Growth ◽  
Multiple Languages

The advent of the Internet has caused a significant growth in the number of opinions expressed about products or services on e-commerce websites. Aspect category detection, which is one of the challenging subtasks of aspect-based sentiment analysis, deals with categorizing a given review sentence into a set of predefined categories. Most of the research efforts in this field are devoted to English language reviews, while there are a large number of reviews in other languages that are left unexplored. In this paper, we propose a multilingual method to perform aspect category detection on reviews in different languages, which makes use of a deep convolutional neural network with multilingual word embeddings. To the best of our knowledge, our method is the first attempt at performing aspect category detection on multiple languages simultaneously. Empirical results on the multilingual dataset provided by SemEval workshop demonstrate the effectiveness of the proposed method1.

scholarly journals Material Critique for Touchstone 3rd Edition: on corpus analysis and spoken grammar

2019 ◽  
Vol 1 (2) ◽  
pp. 34
Author(s):  
Entusiastik -
Keyword(s):  
Natural Language ◽  
English Language ◽  
Spoken Language ◽  
Corpus Analysis ◽  
English Language Teaching ◽  
Free Access ◽  
Lexical Bundles ◽  
Language Analysis ◽  
British National Corpus ◽  
National Corpus

This paper analysed the use of corpus and spoken language features in the English Language Teaching (ELT) coursebook “Touchstone”. The corpus analysis was carried out by using the British National Corpus (BNC) which was chosen for its easy and free access. In doing the spoken language analysis, I refer to McCarthy and Carter’s (2015, p.5) argument which take the grammar of conversation as ‘the benchmark for a grammar of speaking’ by considering features such as ellipsis, heads and teailsm lexical bundles, and vagueness. The analysis indicated that the language used in this coursebook signified a certain level of authentic and natural language, although areas of improvement were also found.

scholarly journals On Some Universal and Specific Semantic and Cognitive Aspects of the Emotive Joy in English

2017 ◽  
Vol 13 (1-2 (17)) ◽  
pp. 92-102
Author(s):  
Shushanik Melik-Adamyan
Keyword(s):  
Positive Emotion ◽  
English Language ◽  
Corpus Analysis ◽  
Metaphor Analysis ◽  
Linguistic Study ◽  
Open The Doors ◽  
The Given ◽  
The Doors

The most widespread distinction made between emotions seems to be their being positive or negative; however, there is much more to their linguistic study. Joy is seen as one of the primary emotions and is thus indispensable as an example of a positive emotion, which can open the doors to the understanding of emotives in the English language and their universal and specific aspects. The paper aims at a better understanding of both universal and specific semantic and cognitive aspects of the given emotion which in linguistics is studied under the term of an emotive. To achieve this purpose the methods of corpus analysis and metaphor analysis have been applied. The study has revealed that positive denotation and almost similar levels of intensity are the universal semantic and cognitive features of the emotive joy.

scholarly journals Polarity Classification Tool for Sentiment Analysis in Malay Language

2019 ◽  
Vol 8 (3) ◽  
pp. 259
Author(s):  
Normi Sham Awang Abu Bakar ◽  
Ros Aziehan Rahmat ◽  
Umar Faruq Othman
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
English Language ◽  
Sentiment Lexicon ◽  
The Social ◽  
Polarity Classification ◽  
Media Channels ◽  
Classification Tool

<p>The popularity of the social media channels has increased the interest among researchers in the sentiment analysis(SA) area. One aspect of the SA research is the determination of the polarity of the comments in the social media, i.e. positive, negative, and neutral. However, there is a scarcity of Malay sentiment analysis tools because most of the work in the literature discuss the polarity classification tool in English. This paper presents the development of a polarity classification tool called Malay Polarity Classification Tool(MaCT). This tool is developed based on the AFINN sentiment lexicon for English language. We have attempted to translate each word in AFINN to its Malay equivalent and later, use the lexicon to collect the sentiment data from Twitter. The Twitter data are then classified into positive, negative, and neutral. For the validation purpose, we collect 400 positive tweets, 400 negative tweets, and 200 neutral tweets, and later, run the tweets through our sentiment lexicon and found 90% score for precision, recall and accuracy. Our main contribution in the research is the new AFINN translation for Malay language and also the classification of the sentiment data.</p>

The semantics of evaluational adjectives

2019 ◽  
Vol 26 (3) ◽  
pp. 308-342 ◽  
Author(s):  
Cliff Goddard ◽  
Maite Taboada ◽  
Radoslava Trnavac
Keyword(s):  
Sentiment Analysis ◽  
Semantic Analysis ◽  
Statistical Information ◽  
Automatic Identification ◽  
First Person ◽  
Lexical Semantic ◽  
Language Technology ◽  
Cognitive Evaluation ◽  
Technology Applications

Abstract We apply the Natural Semantic Metalanguage (NSM) approach (Goddard & Wierzbicka 2014) to the lexical-semantic analysis of English evaluational adjectives and compare the results with the picture developed in the Appraisal Framework (Martin & White 2005). The analysis is corpus-assisted, with examples mainly drawn from film and book reviews, and supported by collocational and statistical information from WordBanks Online. We propose NSM explications for 15 evaluational adjectives, arguing that they fall into five groups, each of which corresponds to a distinct semantic template. The groups can be sketched as follows: “First-person thought-plus-affect”, e.g. wonderful; “Experiential”, e.g. entertaining; “Experiential with bodily reaction”, e.g. gripping; “Lasting impact”, e.g. memorable; “Cognitive evaluation”, e.g. complex, excellent. These groupings and semantic templates are compared with the classifications in the Appraisal Framework’s system of Appreciation. In addition, we are particularly interested in sentiment analysis, the automatic identification of evaluation and subjectivity in text. We discuss the relevance of the two frameworks for sentiment analysis and other language technology applications.

Sign in / Sign up

Export Citation Format

Share Document