sentiwordnet [iit-bombay]
DESCRIPTION
A presentation describing Sentiwordnet - a dictionary of synsets annotated with their sentiment.TRANSCRIPT
Paper PresentationSentiWordNet by
Andrea Esuli and Fabrizio Sebastiani
Sagar Ahire [133050073]
Roadmap● Introduction to Sentiment Analysis● Introduction to Sentiwordnet● Building of Sentiwordnet● Enhancements in 3.0
Roadmap: We Are Here● Introduction to Sentiment Analysis● Introduction to Sentiwordnet● Building of Sentiwordnet● Enhancements in 3.0
Introduction to Sentiment Analysis● The task of identifying the opinion expressed
by a document.● Can be carried out at various levels:
○ Word level○ Sentence level○ Document level○ Aspect level, etc.
Tasks in Sentiment Analysis● Determining Text SO-Polarity
○ Subjective vs. Objective● Determining Text PN-Polarity
○ Positive vs. Negative● Determining Strength of Text PN-Polarity
○ Weakly Positive vs. Strongly Positive○ Weakly Negative vs. Strongly Negative○ Star Rating
Tasks in Sentiment Analysis● Determining Text SO-Polarity
○ Subjective vs. Objective● Determining Text PN-Polarity
○ Positive vs. Negative● Determining Strength of Text PN-Polarity
○ Weakly Positive vs. Strongly Positive○ Weakly Negative vs. Strongly Negative○ Star Rating
Tasks in Sentiment Analysis● Determining Text SO-Polarity
○ Subjective vs. Objective● Determining Text PN-Polarity
○ Positive vs. Negative● Determining Strength of Text PN-Polarity
○ Weakly Positive vs. Strongly Positive○ Weakly Negative vs. Strongly Negative○ Star Rating
Roadmap: We Are Here● Introduction to Sentiment Analysis● Introduction to Sentiwordnet● Building of Sentiwordnet● Enhancements in 3.0
Introduction to Sentiwordnet● Sentiwordnet is a sentiment lexicon
associating sentiment information to each wordnet synset.
● Sentiwordnet = Wordnet + Sentiment Information
Sentiment InformationFor each wordnet synset s, the following information is available in Sentiwordnet:● Positive Score Pos(s)● Negative Score Neg(s)● Objective Score Obj(s)
Pos(s) + Neg(s) + Obj(s) = 1
Roadmap: We Are Here● Introduction to Sentiment Analysis● Introduction to Sentiwordnet● Building of Sentiwordnet● Enhancements in 3.0
Building Sentiwordnet● Trained a set of 8 ternary (P vs. N vs. O)
classifiers, differing in○ Training Set○ Learning Algorithm
● Scored each synset based on no of classifiers:○ P score = No of classifiers stating Positive / 8○ N score = No of classifiers stating Negative / 8○ O score = No of classifiers stating Objective / 8
Classifiers: Training Sets● Used semi-supervised approach starting
with a seed set of paradigmatic synsets (such as nice, nasty, etc.)
● Performed ‘k’ iterations of expansion using Wordnet lexical relations○ Direct antonymy○ Similarity○ Derived from○ Pertains to○ Attribute○ Also see
Classifiers: Training Sets● Obtained 4 training sets for the following ‘k’:
○ 0○ 2○ 4○ 6
Classifiers: Learning Algorithms● The learning algorithms used were:
○ SVM○ Rocchio
● Thus all combinations of 4 training sets and 2 learners yield 8 classifiers
Classifiers: Assigning Categories● Each ternary classifier is a sum of 2 binary
classifiers:○ Positive vs. Not Positive○ Negative vs. Not Negative
● Categories are assigned as:
P NP
N Objective Negative
NN Positive Objective
Classifiers: Observations● Effect of ‘k’:
○ Low ‘k’ -> Low Recall, High Precision○ High ‘k’ -> High Recall, Low Precision
● Effect of learning algorithm:○ SVM -> Favours set with higher cardinality○ Rocchio -> Equal prior probabilities
Statistical Results:Average Scores
Part of Speech Positive Negative Objective
Adjectives 0.106 0.151 0.743
Names 0.022 0.034 0.944
Verbs 0.026 0.034 0.940
Adverbs 0.235 0.067 0.698
All 0.043 0.054 0.903
Roadmap: We Are Here● Introduction to Sentiment Analysis● Introduction to Sentiwordnet● Building of Sentiwordnet● Enhancements in 3.0
Random Walk● Views Wordnet as a graph and performs
random walk on it● Updates P, N and O values till process
converges● Edge from s1 to s2 if s1 occurs in gloss of s2
Random Walk● Two random walks are performed:
○ P Score○ N Score
● O Score is assigned so that P + N + O = 1
WebsiteSentiwordnet is available at:http://sentiwordnet.isti.cnr.it
Major References● SentiWordNet: A Publicly Available Lexical
Resource for Opinion Mining by Andrea Esuli, Fabrizio Sebastiani, 2006
● SentiWordNet 3.0: An Enhanced Lexical Resource for Sentiment Analysis and Opinion Mining by Stefano Baccianella, Andrea Esuli, and Fabrizio Sebastiani, 2010
Other References● Sentiment Analysis and Opinion Mining by Bing Liu,
2012
Further Plan● Wordnet-Affect (2004) by Carlo Strapparava,
Alessandro Valitutti in proceedings of the 4th International Conference of Language Resources and Evaluation (LREC), Lisbon - IN PROGRESS
● Lexicon-based Methods in Sentiment Analysis (2011) by Maite Taboada, Julian Brooke, Milan Tofiloski, Kimberly Voll, Manfred Stede in the Journal of Computational Linguistics