Krahasoni metodat

	Minimi i Opinionit	Minieria e Argumenteve	Analiza e ndjenjave	Klasifikimi i Tekstit
Fusha	Nxjerrja e tekstit	Nxjerrja e tekstit	Nxjerrja e tekstit	Nxjerrja e tekstit
Familja	Process / pipeline	Process / pipeline	Process / pipeline	Process / pipeline
Viti i origjinës	2012	2016	—	—
Krijuesi	Bing Liu	Lippi & Torroni (state-of-the-art survey)	—	—
Lloji	NLP information-extraction task	NLP information-extraction task	NLP text-classification task	Supervised NLP classification task
Burimi themelues	Liu, B. (2012). Sentiment Analysis and Opinion Mining. Morgan & Claypool.	Lippi, M. & Torroni, P. (2016). Argumentation Mining: State of the Art and Emerging Trends. ACM Transactions on Internet Technology, 16(2), Article 10, 1-25.	Pang, B. & Lee, L. (2008). Opinion Mining and Sentiment Analysis. Foundations and Trends in Information Retrieval, 2(1-2), 1-135.	Joachims, T. (1998). Text Categorization with Support Vector Machines: Learning with Many Relevant Features. ECML 1998. Lecture Notes in Computer Science, vol 1398. Springer.
Emërtime të tjera	aspect-based sentiment analysis, opinion extraction, Görüş Madenciliği (Opinion Mining)	argumentation mining, argument extraction, Argüman Madenciliği	opinion mining, polarity detection, duygu analizi	text categorization, document classification, topic classification, metin sınıflandırma
Të lidhura	3	4	3	4
Përmbledhja	Opinion mining is a natural-language-processing task that systematically extracts and analyses user opinions about a product, service, or topic — identifying the specific features (aspects) being discussed, the sentiment expressed toward each, and the opinion holders. Consolidated by Bing Liu (2012), it goes beyond a single document-level label to produce structured aspect–opinion–holder records.	Argument mining is a natural-language-processing task that automatically detects claims, premises and the argumentative structures that link them within text. Consolidated as a field by Lippi and Torroni's 2016 state-of-the-art survey, it is applied to scientific writing, legal documents and debate analysis to turn free-form argumentation into structured, analysable units.	Sentiment analysis, also called opinion mining, is a natural-language-processing task that detects the emotional tone of text — typically classifying it as positive, negative, or neutral. It turns unstructured opinion text into structured, quantifiable polarity signals using one of three families of approaches: sentiment lexicons, trained machine-learning classifiers, or pretrained transformer models.	Text classification, also called text categorization, is a supervised natural-language-processing task that automatically assigns documents to predefined categories. Building on the support-vector-machine approach to text categorization established by Joachims (1998) and consolidated in the text-mining literature by Aggarwal and Zhai (2012), it powers tasks such as spam detection and topic classification by learning from labelled examples.