Concept-Based Sentiment Analysis for Opinion Texts with Multiple-Languages

2016 ◽  
pp. 27-36 ◽  
Author(s):  
Jantima Polpinij ◽  
Natthakit Srikanjanapert ◽  
Chetarin Wongsin
Keyword(s):  
Sentiment Analysis ◽  
Multiple Languages

Sentiment analysis in multiple languages

2008 ◽  
Vol 26 (3) ◽  
pp. 1-34 ◽  
Author(s):  
Ahmed Abbasi ◽  
Hsinchun Chen ◽  
Arab Salem
Keyword(s):  
Sentiment Analysis ◽  
Multiple Languages

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.

Sentiment Analysis of Code-Mixed Text: A Review

2021 ◽  
Vol 12 (3) ◽  
pp. 2469-2478
Author(s):  
Nurul Husna Mahadzir Et.al
Keyword(s):  
Information Retrieval ◽  
Text Mining ◽  
Sentiment Analysis ◽  
State Of The Art ◽  
The State ◽  
The Internet ◽  
Qualitative Comparison ◽  
Analysis Process ◽  
Active Research ◽  
Multiple Languages

In recent times, sentiment analysis has become one of the most active research and progressively popular areas in information retrieval and text mining. To date, sentiment analysis has been applied in various domains such as product, movie, sport and political reviews. Most of the previous work in this field has focused on analyzing only a single language, especially English. However, with the need of globalization and the increasing number of the Internet used worldwide; it is common to see the post written in multiple languages. Moreover, in an unstructured content like Twitter posts, people tend to mix languages in one sentence, which make sentiment analysis process even harder and more challenging. This paper reviews the state-of-the-art of sentiment analysis for code-mixed, which includes the detail discussions of each focus area, qualitative comparison and limitations of current approaches. This paper also highlights challenges along this line of research and suggests several recommendations for future works that should be explored.

Feature Based Sentiment Analysis of Tweets in Multiple Languages

2014 ◽  
pp. 109-124 ◽  
Author(s):  
Maike Erdmann ◽  
Kazushi Ikeda ◽  
Hiromi Ishizaki ◽  
Gen Hattori ◽  
Yasuhiro Takishima
Keyword(s):  
Sentiment Analysis ◽  
Feature Based ◽  
Multiple Languages

scholarly journals Multilingual Review-aware Deep Recommender System via Aspect-based Sentiment Analysis

2021 ◽  
Vol 39 (2) ◽  
pp. 1-33
Author(s):  
Peng Liu ◽  
Lemei Zhang ◽  
Jon Atle Gulla
Keyword(s):  
Sentiment Analysis ◽  
Contextual Information ◽  
International Markets ◽  
Superior Performance ◽  
Fine Grained ◽  
Learning Techniques ◽  
Rating Prediction ◽  
Real World Datasets ◽  
Multiple Languages ◽  
Inherent Ambiguity

With the dramatic expansion of international markets, consumers write reviews in different languages, which poses a new challenge for Recommender Systems (RSs) dealing with this increasing amount of multilingual information. Recent studies that leverage deep-learning techniques for review-aware RSs have demonstrated their effectiveness in modelling fine-grained user-item interactions through the aspects of reviews. However, most of these models can neither take full advantage of the contextual information from multilingual reviews nor discriminate the inherent ambiguity of words originated from the user’s different tendency in writing. To this end, we propose a novel Multilingual Review-aware Deep Recommendation Model (MrRec) for rating prediction tasks. MrRec mainly consists of two parts: (1) Multilingual aspect-based sentiment analysis module (MABSA), which aims to jointly extract aligned aspects and their associated sentiments in different languages simultaneously with only requiring overall review ratings. (2) Multilingual recommendation module that learns aspect importances of both the user and item with considering different contributions of multiple languages and estimates aspect utility via a dual interactive attention mechanism integrated with aspect-specific sentiments from MABSA. Finally, overall ratings can be inferred by a prediction layer adopting the aspect utility value and aspect importance as inputs. Extensive experimental results on nine real-world datasets demonstrate the superior performance and interpretability of our model.

Study of Sentiment of Governor's Election Opinion in 2018

2018 ◽  
pp. 231-238
Author(s):  
Agung Eddy Suryo Saputro ◽  
Khairil Anwar Notodiputro ◽  
Indahwati A
Keyword(s):  
Sentiment Analysis ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Addition Method ◽  
Sentiment Mining ◽  
Positive Sentiment ◽  
Trending Topics ◽  
Election News

In 2018, Indonesia implemented a Governor's Election which included 17 provinces. For several months before the Election, news and opinions regarding the Governor's Election were often trending topics on Twitter. This study aims to describe the results of sentiment mining and determine the best method for predicting sentiment classes. Sentiment mining is based on Lexicon. While the methods used for sentiment analysis are Naive Bayes and C5.0. The results showed that the percentage of positive sentiment in 17 provinces was greater than the negative and neutral sentiments. In addition, method C5.0 produces a better prediction than Naive Bayes.

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.

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