Browsing by Subject "sentiment analysis"

Now showing 1 - 5 of 5

Access status: Open Access ,
Adapting a constituency parser to user-generated content in polish opinion mining
(Wydawnictwa AGH, 2016) Pluwak, Agnieszka; Korczyński, Wojciech; Kisiel-Dorohinicki, Marek
The paper focuses on the adjustment of NLP tools for Polish, e.g., morphological analyzers and parsers, to user-generated content (UGC). The authors discuss two rule-based techniques applied to improve their efficiency: pre-processing (text normalization) and parser adaptation (modified segmentation and parsing rules). A new solution to handle OOVs based on inflectional translation is also offered.
Access status: Open Access ,
An improved context-aware sentiment analysis of student comments on social networks based on ChatGPT
(Wydawnictwa AGH, 2025) Qaffas, Alaa Asim
The widespread use of social networks has provided a variety of active, dynamic, and popular platforms for students to express their opinions and sentiments. These data are increasingly being exploited and integrated into university information systems to better govern and manage universities and improve educational quality. The analysis of such data can offer valuable insights into student experiences and attitudes towards various educational aspects including courses, professors, events, and facilities. However, automatic opinion mining in this context is challenging due to the difficulty of analyzing some languages such as Arabic, the variety of used languages, the presence of informal language, the use of emoticons and emoji, sarcasm, and the need to consider the surrounding context. To deal with all these challenges, we propose a novel approach for an effective sentiment analysis of student comments on the X platform (Twitter). The proposed approach allows the collection of student comments from public Twitter pages and automatically classifies comments into positive, negative, and neutral. The new approach is based on ChatGPT capabilities, supports three languages: English, Arabic, and colloquial Arabic, and integrates a new scoring method that measures both the positiveness and subjectivity of student comments. Experiments performed on simulated and real public Twitter pages of five Saudi high education institutions showed the performance of the proposed tool to analyze and summarize collected data automatically.
Access status: Restricted ,
Analiza opinii przy użyciu uczenia głębokiego
(Data obrony: 2018-01-22) Flis, Marcin
Wydział Elektrotechniki, Automatyki, Informatyki i Inżynierii Biomedycznej
Access status: Open Access ,
Analysis of data pre-processing methods for sentiment analysis of reviews
(Wydawnictwa AGH, 2019) Parlar, Tuba; Özel, Selma Ayşe; Song, Fei
The goals of this study are to analyze the effects of data pre-processing methods for sentiment analysis and determine which of these pre-processing methods (and their combinations) are effective for English as well as for an agglutinative language like Turkish. We also try to answer the research question of whether there are any differences between agglutinative and non-agglutinative languages in terms of pre-processing methods for sentiment analysis. We find that the performance results for the English reviews are generally higher than those for the Turkish reviews due to the differences between the two languages in terms of vocabularies, writing styles, and agglutinative property of the Turkish language.
Access status: Open Access ,
Building sentiment lexicons based on recommending services for the Polish language
(Wydawnictwa AGH, 2016) Gliwa, Bogdan; Zygmunt, Anna; Dąbrowski, Michał
Sentiment analysis has become a prominent area of research in computer science. It has numerous practical applications, e.g., evaluating customer satisfaction, identifying product promoters. Many methods employed in this task require language resources such as sentiment lexicons, which are unavailable for the Polish language. Such lexicons contain words annotated with their emotional polarization, but the manual creation of sentiment lexicons is very tedious. Therefore, this paper addresses this issue and describes a new method of building sentiment lexicons automatically based on recommending services. Next, the built lexicons were used in the task of sentiment classification.