Temat: Opinion Analysis - Katalog OPAC zbiorów

Skocz do pozycji: 1.

Tytuł:: A Comprehensive study: - Sarcasm detection in sentimental analysis
Autorzy:: Ratawal, Yamini
Tayal, Devendra
Powiązania:: https://bibliotekanauki.pl/articles/1159725.pdf
Data publikacji:: 2018
Wydawca:: Przedsiębiorstwo Wydawnictw Naukowych Darwin / Scientific Publishing House DARWIN
Tematy:: Sentimental analysis
Web mining
deep learning
machine learning
opinion mining
text mining
Opis:: Sarcasm detection is one of the active research area in sentimental analysis. However this paper talks about one of the recent issue in sentimental analysis that us sarcasm detection. In our work, we have described different techniques used in sarcasm detection that helps a novice researcher in efficient way. This paper represent different methodologies of carrying out research in this field.
Źródło:: World Scientific News; 2018, 113; 1-9
2392-2192
Pojawia się w:: World Scientific News
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 2.

Tytuł:: Customer product review summarization over time for competitive intelligence
Autorzy:: Amarouche, Kamal
Benbrahim, Houda
Kassou, Ismail
Powiązania:: https://bibliotekanauki.pl/articles/950925.pdf
Data publikacji:: 2018
Wydawca:: Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów
Tematy:: feature extraction
fuzzy logic
competitive intelligence
opinion mining
opinion summarization
sentiment analysis
SentiWordNet
ekstrakcja cech
logika rozmyta
wywiad konkurencyjny
eksploracja opinii
podsumowanie opinii
analiza nastrojów
Opis:: Nowadays, Customer’s product reviews can be widely found on the Web, be it in personal blogs, forums, or ecommerce websites. They contain important products’ information and therefore became a new data source for competitive intelligence. On that account, these reviews need to be analyzed and summarized in order to help the leader of an entity (company, brand, etc.) to make appropriate decisions in an efective way. However, most previous review summarization studies focus on summarizing sentiment distribution toward different product features without taking into account that the real advantages and disadvantages of a product clarify over time. For this reason, in this work we aim to propose a new system for product opinion summarization which depends on the time when reviews are expressed and that covers the sentiments change about product features. The proposed system firstly, generates a summary based on product features in order to give more accurate and efficient information about different features. secondly, classify the product based on its features in its appropriate class (good, medium or bad product) using a fuzzy logic system. The experimental results demonstrate the effectiveness of the proposed system to generate the real image of a product and its features in reviews.
Źródło:: Journal of Automation Mobile Robotics and Intelligent Systems; 2018, 12, 4; 70-82
1897-8649
2080-2145
Pojawia się w:: Journal of Automation Mobile Robotics and Intelligent Systems
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 3.

Tytuł:: Sentiment Classification of Bank Clients’ Reviews Written in the Polish Language
Analiza sentymentu na podstawie polskojęzycznych recenzji klientów banku
Autorzy:: Idczak, Adam Piotr
Powiązania:: https://bibliotekanauki.pl/articles/2033889.pdf
Data publikacji:: 2021-06-30
Wydawca:: Uniwersytet Łódzki. Wydawnictwo Uniwersytetu Łódzkiego
Tematy:: analiza sentymentu
klasyfikacja dokumentów
textmining
regresja logistyczna
naiwny klasyfikator Bayesa
sentiment analysis
opinion mining
text classification
text mining
logistic regression
naive Bayes classifier
Opis:: It is estimated that approximately 80% of all data gathered by companies are text documents. This article is devoted to one of the most common problems in text mining, i.e. text classification in sentiment analysis, which focuses on determining the sentiment of a document. A lack of defined structure of the text makes this problem more challenging. This has led to the development of various techniques used in determining the sentiment of a document. In this paper, a comparative analysis of two methods in sentiment classification, a naive Bayes classifier and logistic regression, was conducted. Analysed texts are written in the Polish language and come from banks. The classification was conducted by means of a bag‑of‑n‑grams approach, where a text document is presented as a set of terms and each term consists of n words. The results show that logistic regression performed better.
Szacuje się, że około 80% wszystkich danych gromadzonych i przechowywanych w systemach informacyjnych przedsiębiorstw ma postać dokumentów tekstowych. Artykuł jest poświęcony jednemu z podstawowych problemów textminingu, tj. klasyfikacji tekstów w analizie sentymentu, która rozumiana jest jako badanie wydźwięku tekstu. Brak określonej struktury dokumentów tekstowych jest przeszkodą w realizacji tego zadania. Taki stan rzeczy wymusił rozwój wielu różnorodnych technik ustalania sentymentu dokumentów. W artykule przeprowadzono analizę porównawczą dwóch metod badania sentymentu: naiwnego klasyfikatora Bayesa oraz regresji logistycznej. Badane teksty są napisane w języku polskim, pochodzą z banków i mają charakter marketingowy. Klasyfikację przeprowadzono, stosując podejście bag‑of‑n‑grams. W ramach tego podejścia dokument tekstowy wyrażony jest za pomocą podciągów składających się z określonej liczby n wyrazów. Uzyskane wyniki pokazały, że lepiej spisała się regresja logistyczna.
Źródło:: Acta Universitatis Lodziensis. Folia Oeconomica; 2021, 2, 353; 43-56
0208-6018
2353-7663
Pojawia się w:: Acta Universitatis Lodziensis. Folia Oeconomica
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 4.

Tytuł:: Aspect-based sentiment classification model employing whale-optimized adaptive neural network
Autorzy:: Balaganesh, Nallathambi
Muneeswaran, K.
Powiązania:: https://bibliotekanauki.pl/articles/2173622.pdf
Data publikacji:: 2021
Wydawca:: Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:: aspect-based sentiment analysis
whale optimization algorithm
artificial neural network
opinion mining
analiza nastrojów oparta na aspektach
algorytm optymalizacji wielorybów
sztuczna sieć neuronowa
eksploracja opinii
Opis:: Nowadays in e-commerce applications, aspect-based sentiment analysis has become vital, and every consumer started focusing on various aspects of the product before making the purchasing decision on online portals like Amazon, Walmart, Alibaba, etc. Hence, the enhancement of sentiment classification considering every aspect of products and services is in the limelight. In this proposed research, an aspect-based sentiment classification model has been developed employing sentiment whale-optimized adaptive neural network (SWOANN) for classifying the sentiment for key aspects of products and services. The accuracy of sentiment classification of the product and services has been improved by the optimal selection of weights of neurons in the proposed model. The promising results are obtained by analyzing the mobile phone review dataset when compared with other existing sentiment classification approaches such as support vector machine (SVM) and artificial neural network (ANN). The proposed work uses key features such as the positive opinion score, negative opinion score, and term frequency-inverse document frequency (TF-IDF) for representing each aspect of products and services, which further improves the overall effectiveness of the classifier. The proposed model can be compatible with any sentiment classification problem of products and services.
Źródło:: Bulletin of the Polish Academy of Sciences. Technical Sciences; 2021, 69, 3; art. no. e137271
0239-7528
Pojawia się w:: Bulletin of the Polish Academy of Sciences. Technical Sciences
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 5.

Tytuł:: Aspect-based sentiment classification model employing whale-optimized adaptive neural network
Autorzy:: Balaganesh, Nallathambi
Muneeswaran, K.
Powiązania:: https://bibliotekanauki.pl/articles/2128172.pdf
Data publikacji:: 2021
Wydawca:: Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:: aspect-based sentiment analysis
whale optimization algorithm
artificial neural network
opinion mining
analiza nastrojów oparta na aspektach
algorytm optymalizacji wielorybów
sztuczna sieć neuronowa
eksploracja opinii
Opis:: Nowadays in e-commerce applications, aspect-based sentiment analysis has become vital, and every consumer started focusing on various aspects of the product before making the purchasing decision on online portals like Amazon, Walmart, Alibaba, etc. Hence, the enhancement of sentiment classification considering every aspect of products and services is in the limelight. In this proposed research, an aspect-based sentiment classification model has been developed employing sentiment whale-optimized adaptive neural network (SWOANN) for classifying the sentiment for key aspects of products and services. The accuracy of sentiment classification of the product and services has been improved by the optimal selection of weights of neurons in the proposed model. The promising results are obtained by analyzing the mobile phone review dataset when compared with other existing sentiment classification approaches such as support vector machine (SVM) and artificial neural network (ANN). The proposed work uses key features such as the positive opinion score, negative opinion score, and term frequency-inverse document frequency (TF-IDF) for representing each aspect of products and services, which further improves the overall effectiveness of the classifier. The proposed model can be compatible with any sentiment classification problem of products and services.
Źródło:: Bulletin of the Polish Academy of Sciences. Technical Sciences; 2021, 69, 3; e137271, 1--8
0239-7528
Pojawia się w:: Bulletin of the Polish Academy of Sciences. Technical Sciences
Dostawca treści:: Biblioteka Nauki

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