TAILIEUCHUNG - Báo cáo khoa học: "Examining the Role of Linguistic Knowledge Sources in the Automatic Identification and Classification of Reviews"

This paper examines two problems in document-level sentiment analysis: (1) determining whether a given document is a review or not, and (2) classifying the polarity of a review as positive or negative. We first demonstrate that review identification can be performed with high accuracy using only unigrams as features. We then examine the role of four types of simple linguistic knowledge sources in a polarity classification system. | Examining the Role of Linguistic Knowledge Sources in the Automatic Identification and Classification of Reviews Vincent Ng and Sajib Dasgupta and S. M. Niaz Arifin Human Language Technology Research Institute University of Texas at Dallas RichardsOn TX 75083-0688 vince sajib arif @ Abstract This paper examines two problems in document-level sentiment analysis 1 determining whether a given document is a review or not and 2 classifying the polarity of a review as positive or negative. We first demonstrate that review identification can be performed with high accuracy using only unigrams as features. We then examine the role of four types of simple linguistic knowledge sources in a polarity classification system. 1 Introduction Sentiment analysis involves the identification of positive and negative opinions from a text segment. The task has recently received a lot of attention with applications ranging from multiperspective question-answering . Cardie et al. 2004 to opinion-oriented information extraction . Riloff et al. 2005 and summarization . Hu and Liu 2004 . Research in sentiment analysis has generally proceeded at three levels aiming to identify and classify opinions from documents sentences and phrases. This paper examines two problems in document-level sentiment analysis focusing on analyzing a particular type of opinionated documents reviews. The first problem polarity classification has the goal of determining a review s polarity positive thumbs up or negative thumbs down . Recent work has expanded the polarity classification task to additionally handle documents expressing a neutral sentiment. Although studied fairly extensively polarity classification remains a challenge to natural language processing systems. We will focus on an important linguistic aspect of polarity classification examining the role of a variety of simple yet under-investigated linguistic knowledge sources in a learning-based polarity classification system. .

• Báo cáo khoa học
• Examining the Role
• Linguistic Knowledge Sources in the Automatic Identification
• Classification of Reviews
• báo cáo khoa học
• mô hình ngôn ngữ
• xử lý ngôn ngữ tự nhiên
• Repurchase intention
• Word of mouth
• Consumers’ satisfaction
• Applying Structural Equation Modeling
• Shopping environments
• Conduct disorders
• Behaviour problems
• Family support
• Social work
• Social support
• Parenting behavior
• Neural correlates
• Maternal caregivin
• Double blind
• Functional Magnetic Resonance Imaging
• BMC Cancer
• Colorectal cancer
• Prescription drugs
• Gut microbiome
• Metagenomics sequencing
• Examining Language
• Literate Lives
• Literacy Coaching
• Literature Cited
• Observation Form
• Coaching Role
• BMC Bioinformatics
• Mediation analysis
• Mediated effects
• Direct effects
• Unmeasured confounding
• Population stratification
• Human resources
• Volunteer motivation
• Volunteer intention
• Tourism events
• Volunteers play
• Marketing in the Connected Age
• Electronic word of mouth (e WOM)
• Online consumer reviews (OCRs)
• Online review helpfulness (ORH)
• Review rating
• Review length
• Review sentiment