A Bootstrapping Algorithm for Learning the Polarity of Words

نویسندگان

  • António Paulo-Santos
  • Hugo Gonçalo Oliveira
  • Carlos Ramos
  • Nuno C. Marques
چکیده

Polarity lexicons are lists of words (or meanings) where each entry is labelled as positive, negative or neutral. These lists are not available for different languages and specific domains. This work proposes and evaluates a new algorithm to classify words as positive, negative or neutral, relying on a small seed set of words, a common dictionary and a propagation algorithm. We evaluate the positive and negative polarity propagation of words, as well as the neutral polarity. The propagation is evaluated with different settings and lexical resources.

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Sentiment Analysis Based on Expanded Aspect and Polarity-Ambiguous Word Lexicon

This paper focuses on the task of disambiguating polarity-ambiguous words and the task is reduced to sentiment classification of aspects, which we refer to sentiment expectation instead of semantic orientation widely used in previous researches. Polarity-ambiguous words refer to words like” large, small, high, low ”, which pose a challenging task on sentiment analysis. In order to disambiguate ...

متن کامل

Design and implementation of Persian spelling detection and correction system based on Semantic

Persian Language has a special feature (grapheme, homophone, and multi-shape clinging characters) in electronic devices. Furthermore, design and implementation of NLP tools for Persian are more challenging than other languages (e.g. English or German). Spelling tools are used widely for editing user texts like emails and text in editors.  Also developing Persian tools will provide Persian progr...

متن کامل

Bootstrapping Supervised Machine-learning Polarity Classifiers with Rule-based Classification

In this paper, we explore the effectiveness of bootstrapping supervised machine-learning polarity classifiers using the output of domain-independent rule-based classifiers. The benefit of this method is that no labeled training data are required. Still, this method allows to capture in-domain knowledge by training the supervised classifier on in-domain features, such as bag of words. We investi...

متن کامل

Unsupervised Detection of Downward-Entailing Operators By Maximizing Classification Certainty

We propose an unsupervised, iterative method for detecting downward-entailing operators (DEOs), which are important for deducing entailment relations between sentences. Like the distillation algorithm of Danescu-Niculescu-Mizil et al. (2009), the initialization of our method depends on the correlation between DEOs and negative polarity items (NPIs). However, our method trusts the initialization...

متن کامل

Bootstrapping polarity classifiers with rule-based classification

In this article, we examine the effectiveness of bootstrapping supervised machine-learning polarity classifiers with the help of a domain-independent rulebased classifier that relies on a lexical resource, i.e., a polarity lexicon and a set of linguistic rules. The benefit of this method is that though no labeled training data are required, it allows a classifier to capture in-domain knowledge ...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2012