A Framework for Automated Corpus Generationfor Semantic Sentiment Analysis

Amna Asmi and Tanko Ishaya, Member, IAENG

Proceedings of the World Congress on Engineering 2012 Vol IWCE 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 (http://www.medhelp.org/forums/list)

• 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

Thank You

GIFs courtesy : http://www.retrojunkie.com/