Hassan & Radev ACL 2010

Citation

Ahmed Hassan and Dragomir R. Radev. 2010. Identifying text polarity using random walks. In ACL 2010.

Online Version

online version

Summary

This paper shows a method for identifying the polarity of words which addresses the topic of Polarity Classification of words.

The method is based on the observation that a random walk starting at a given word is more likely to hit another word with the same semantic orientation before hitting a word with a different semantic orientation. It applies random walk model to a large word relatedness graph and produce a polarity estimate for any given word.

This method can be used in a semi-supervised setting where a training set of labeled words is used, and in an unsupervised setting where only a handful of seeds is used to define the two polarity classes.

Background

• Network construction

The dataset WordNet is used to construct the network of words.Collect all words in WordNet, and add links between any two words that occurr in the same synset. The resulting graph is a graph $G(W,E)$ where $W$ is a set of word / part-of-speech pairs for all the words in WordNet. $E$ is the set of edges connecting each pair of synonymous words.

• Random walk model

Starting from a word with unknown polarity $i$ , it moves to a node $j$ with probability $P_{ij}$ after the first step. The walk continues until the surfer hits a word with a known polarity.

• First-passage time

It is very similar to the definition of hitting time. The mean first-passage (hitting) time $h(i|k)$ is defined as the average number of steps a random walker, starting in state $i\neq k$, will take to enter state $k$ for the first time. Considering a subset vertices of the graph, then $h(i|S)$ means the average number of steps a random walker, starting in state $i\notin S$, will take to enter a stae $k\in S$ for the first time.

Then it is proven that:

$h(i|S)={\begin{cases}0,&i\in S\\\sum _{j\in V}p_{ij}*h(j|S)+1,&otherwise\end{cases}}$

Algorithm description

1. Construct a word relatedness graph
2. Apply a random walk on the graph
3. Compute the word's hitting time for both the positive and negative sets of vertices
4. If the hitting time for the positive set is greater than for the negative set, than the word is classified as negative. Otherwise, it is classified as positive. The ratio between the two hitting times could be used as an indication of how positive/negative the given word is.

Since computing the hitting time is time consuming especially when the graph is large, a Monte Carlo based estimating algorithm is proposed as such:

Experiment result

Comparing to other methods, this method is quite successful in both the settings of semi-supervised and unsupervised.

• In the setting of using WordNet synonyms and hypernyms to construct the network and test set to the set of adjectives. It out performs the spin-model, bootstrap and short-path method.

• It is also compared to the SO-PMI method in the setting of only 14 seeds. Though SO-PMI with a very large dataset performs slightly better than this method, this method is faster and does not need such large corpus.

Related papers

• Hiroya Takamura, Takashi Inui, and Manabu Okumura.2005. Extracting semantic orientations of words using spin model. In ACL ’05.
• Peter Turney and Michael Littman. 2003. Measuring praise and criticism: Inference of semantic orientation from association. ACM Transactions on Information Systems, 21:315–346.
• Jaap Kamps, Maarten Marx, Robert J. Mokken, and Maarten De Rijke. 2004. Using wordnet to measure semantic orientations of adjectives. In National Institute for, pages 1115–1118.
• Minqing Hu and Bing Liu. 2004. Mining and summarizing customer reviews. In KDD ’04
• Peter D. Turney. 2002. Thumbs up or thumbs down?:semantic orientation applied to unsupervised classification of reviews. In ACL ’02

Study plan

• Article: Random walk
• Article: Monto Carlo Method