A Framework for Automated Corpus Generation for Semantic Sentiment Analysis Amna Asmi and Tanko Ishaya, Member, IAENG Proceedings of the World Congress on Engineering 2012 Vol I WCE 2012, July 4 - 6, 2012, London, U.K.

Introduction Variety of corpora present (WordNet, SentiWordNet and Multi-Perspective Question Answering (MPQA)) Some corpora not large enough Generation and annotation is time consuming and inconsistent. This paper presents a framework for automated generation of corpus for semantic sentiment analysis of user generated web-content

Existing corpora MPQA Movie Review (pang and others, 2002) Varbaul (Sankoff and Cedegan, program based on multivariate analysis) Fidditch (automated parser for English) Automatic Mapping Among Lexico-Grammatical Annotation Models (AMALGAM) International corpus of English (ICE)

Existing Techniques for Sentiment Analysis Direction based text including opinions, sentiments, affects and biases Opinion mining using ML techniques (supervised/ unsupervised) (document /sentence/clause level) Polarity, degree of polarity, features, subjectivity, relationships, identification, affect types, mood classification and ordinal scale

Annotation Process Methodology Grabbing URL, author, subject, text, comments Text broken to sentences Sentence applied with Stanford Dependencies Parser and Penn Treebank Tagging and broken down into clauses Subject-Verb-Object triplet extracted Rules according to POS, negation, punctuation, conjunction is specified using SentiWordNet and WordNet Rules used to extract sentiment, and define polarity and intensity Based on subject and object, and topic/title of sentence of post, subjectivity is calculated

Tools used WordNet SentiWordNet Stanford Parser PennTree Bank UMLS(Unified Medical Language System)

Framework Repository: Wordnet, SentiWordNet dictionaries, UMLS Metathesaurus Rules for sentence, polarity, subjectivity and sentiment identification and analysis Data Pre-processor: Input: Unstructured data from medical forum ( Input cleaned and filtered Captures thread structure, comments of forum, and arranges other info like author, topic, date. Spell checks Split to set of posts and sent to post pre-processor

Framework Post Pre-Processor Splits texts to sentences using Penn Tree Tagger Passes sentences to syntactic parser iteratively Keeps track of start and end of post Syntactic Parser (SP) Collects sentences iteratively and invokes POS tagger Name entities and idioms are identified Identifies dependencies/ relationship Classifies sentence as a question, assertion, comparison, confirmation seeking or confirmation providing

Framework Sentiment Analyser(SA) Extracts sentiment oriented words from each sentence by using relationship info (dependencies within) Polarity Calculator (PC) identifies + and – words. Synonyms used if word is not found Collects synonyms from SentiWordNet Uses UMLS Metathesaurus if synonym not found Rules for polarity identification used

Framework Subjectivity Calculator(SC) Considers POS and relationships Identifies all sentences related to topic Takes nouns and associated info (synonyms, homonyms, meronyms, holonyms and hyponyms) Sentiment Analyser: Takes polarities of sentences marked by SC for post polarity calculation Takes aggregate of all polarities of sentences related to post Generates sentiment frame info for each sentence Frame contains type, subject, object/feature, sentiment oriented word(s), sentiment type (absolute / relative), strength (very weak, weak, average, strong, very strong), polarity of sentence, post index and sentence index Forwards calculated values and info to Sentiment Frame manager

Framework Sentiment Frame Manager Stores all information to a physical location Loads all frames in tree structure at runtime memory on program load Keeps track of changes and appends changes Stored into XML file

Future Work Currently being evaluated using medical based forums Plans to make it general purpose

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