Davidov et al COLING 10

This a Paper reviewed for Social Media Analysis 10-802 in Fall 2012.

Citation

author    = {Dmitry Davidov and
              Oren Tsur and
              Ari Rappoport},
 title     = {Enhanced Sentiment Learning Using Twitter Hashtags and Smileys},
 booktitle = {COLING (Posters)},
 year      = {2010},
 pages     = {241-249},
 ee        = {http://aclweb.org/anthology-new/C/C10/C10-2028.pdf},
 crossref  = {DBLP:conf/coling/2010p},
 bibsource = {DBLP, http://dblp.uni-trier.de}

Online Version

Enhanced sentiment learning using Twitter hashtags and smileys

Summary

The paper proposes a supervised framework for sentiment classification utilizing the Twitter dataset. The paper classifies the sentiment beyond the positive and negative labels by using the 50 Twitter tags and 15 smileys as sentiment labels. The short textual sentences(tweet) of Twitter are sometimes labeled as sentiment tags, which assigns sentiment values to the tweet. The paper utilizes such tagged Twitter data for classification of a wide variety of sentiment types from text. The paper employs different kinds of features and shows that the framework successfully identifies sentiment types of the text(blogs and tweets).

Methodoloy

• Features - The features employed in classifying the sentiments can be broadly divided into 4 distinct types.
  • Single-word features - They are considered as binary features with weight equal to the inverted count in the corpus.
  • n-gram features - The 2-5 length of consecutive words are considered as binary features with the same weighting as for the single-word features.
  • Pattern-based features - The words are classified as High frequency words(HFW) and content words(CW) .
    • A pattern is defined as an ordered sequence of HFW and slots for CW based on the frequency threshold.
    • In this paper, a pattern contains 2-6 HFWs and 1-5 slots for CWs. It is based on the works of
    • The weight for the pattern is assigned to the degree of match of the pattern - Exact match, Sparse match, Incomplete match and No match.
• Punctuation features - The weight assigned is the average weight of the respective features.
  • Length of a sentence.
  • Number of exclamations
  • Number of question marks
  • Number of quotes
  • Number of capital words.

• Classification Algorithm

The algorithm used to assign the sentiment label to test examples is a slight modification of the k-NN algorithm.

Evaluation

Evaluation using cross-validation

The sentiment classification is evaluated using 10-fold cross-validation over the training set. The performance of the algorithm was tested under different feature settings. - Multi-class classification - There are 51 hashtag-based and 16 smiley based labels. The evaluation metric is the average f-score for 10-fold cross validation. The f-score for the random baseline is 0.02. The result is shown in the following table.

The result is significantly better than the random baseline.

- Binary classification The labels are 1 if the sentence contains a particular label or 0 if the sentence does not bear any sentiment. For each of the 50 hashtag-based and 15 smiley-based labels, the binary classification is performed. The result is as shown in the following table.

The results show that binary classification is better than the multi-class classification with a high precision value.

Evaluation with human judges

Amazon Mechanical Turk (AMT) services was used to evaluate the performance of the classifier on test data. Te evaluation was considered correct if one of the tags selected by a human judge for a sentence was one of the 5 tags predicted by the algorithm. The correlation score for this task was $\kappa =0.41$.

Observations

• This work presents a supervised classification framework for which utilizes Twitter hashtags and smileys as proxies for different sentiment types as labels. It contributes to avoiding the need for labor intensive manual annotation, allowing identification and classification of diverse sentiment types of short texts.
• Binary classification of sentiments yields better results than multi-class classification.
• Punctuation, word and pattern features contributes more towards classification performance, as compared to a small marginal boost provided by the the n-gram features. Pattern features provides better performance as compared to the combined effect of the rest of the features.
• Preliminary exploration on inter-sentiment overlap and dependency by two simple techniques of tag occurrence and feature overlap.

Study Plan

K-Nearest Neighbor

Multi-class Classification

Related Work

A similar work on extracting sentiment types on blogs was carried by McDonal et al (2007).

The automated pattern based approach for extracting sentiments is based on Davidov and Rappoport (2006) and Davidov and Rappoport (2008).