Feature Set based on MPQA Corpus

mpqa_features.pickle is a serialized Pandas DataFrame with about 8000 labeled feature vectors that were derived from the annotated MPQA Corpus_ to model phrase and sentence level sentiment (polarity) in news and editorial content.

Basic Idea

The idea is to use "subjectivity clues" - i.e. words that typically indicate expression of subjective opinion or judgement - that have been tagged with a prior polarity (i.e. the sentiment they convey in the absence of any context - neutral, positive, negative or both) to identify text passages that are likely to contain subjective expression. The subjectivity clues were taken from the MPQA Subjectivity Lexicon_.

The subjectivity clues were looked for in the approx. 700 documents in the extended MPQA Corpus. The corpus annotations were used to determine the contextual polarity for each of occurrence. See Wilson, Wiebe, and Hoffmann (2005).

Data Set

The DataFrame in mpqa_features.pickle has the following structure:

Index


The index consists of tuples of the form ``(path, document, count)`` where ``path`` and ``document`` identify the corpus document from which the record was generated and ``count`` is a running number within the document.

Columns

word: int The subjectivity clue (id in the spaCy vocabulary) ``word: string The subjectivity clue (i.e. an occurrence of one of the entries in the `MPQA Subjectivity Lexicon`_) pos: int Part-of-speech (id as assigned by `spaCy`_ as atoken'sposattribute) pos`: string Part-of-speech tag (abbreviation as assigned byspaCy` as a token's pos_ attribute) before: int Word preceding the subjectivity clue (vocabulary id) before_: string Word preceding the subjectivity clue after: int Word following the subjectivity clue (vocabulary id) after_: string Word following the subjectivity clue context: tuple of ints (before, word, after) as vocabulary ids (this is redundant with the individual tokens but helps for visually exploring the data set). context_: tuple if strings (before, word, after) pre_neg: boolean True if there is a negating word within four tokens ahead of the subjectivity clue (not counting double negations such as "not only") post_neg: boolean True if there is a negating word within four tokens after of the subjectivity clue pri_pol: string Prior polarity as given in the MPQA Subjectivity Lexicon (possible values are 'positive', 'negative', 'both', and 'neutral') rel: string Reliability class as given in the MPQA Subjectivity Lexicon (possible values are 'strongsubj', 'weaksubj') c_pol: float Contextual polarity, derived from the contextual polarity and intensity entries in the MPQA Corpus_: uses 1.0 for positive and -1.0 for neagtive polarity (0 for neutral) and multiplies with the intensity annotation (0 for low, 0.75 for medium, 1.5 for high, and 2.0 for extreme). is_int: boolean True if the subjectivity clue itself is an intensifier. The list of itensifiers is taken from the MPQA Arguing Lexicon_. prec_int: boolean True if subjectivity clue is preceded by an intensifier prec_adj: boolean True if subjectivity clue is preceded by an adjective prec_adv: boolean True if subjectivity clue is preceded by an adverb topic: string Topic of the respective article (if available in the corpus annotations) pword: int "Packed id" for word (the subjectivity clue). Between the word, before and after vocabulary ids there are 3,691 different ids, taken from a vocabulary with about 300,000 entries. The "packed" values map these ids onto consecutive integers between 0 and 3,690. pbefore: int Packed id for before pbafter: int Packed id for after

Python Script

The python script mpqa.py was used to construct the labeled feature vectors in mpqa_features.pickle. Run::

python mpqa mkfeat -h

for instructions. Running the script requires (see Licenses for Corpus Content and Annotations_):

Third party python packages pandas and spaCy.
The annotated MPQA Corpus_.
A 'doclist' file that lists all documents to be included in the data set (concatenate and dedupe the partially overlapping doclists that come with MPQA Corpus or use the doclist.combinedUnique file in this repo).
The MPQA Subjectivity Lexicon_ (provided as subjclues.tff in this repo).
A list of intensifiers, available in the MPQA Arguing Lexicon_ (file intensifiers.tff).

To use the mpqa module from within your own script follow this example::

from __future__ import print_function
import pandas as pd
import mpqa

df = pd.DataFrame(columns=mpqa.FEAT_COLS)
for path, fname, topic in mpqa.iter_docs('doclist.combinedUnique'):
    print(path, fname)
    doc = mpqa.Doc(
            mpqa_dir='database.mpqa.2.0',
            path=path,
            fname=fname,
            topic=topic,
            sc_path='subjclues.tff',
            int_path='intensifiers.tff')
    df = df.append(doc.feat_df)

sparse_cols = ['word', 'before', 'after']
pack_cols = mpqa.pack_df(df, sparse_cols)
for c in sparse_cols:
    df['p' + c] = pack_cols[c]

This assumes that you have downloaded and extracted the MPQA Corpus_ to database.mpqa.2.0. The resulting DataFrame df will be the same as the one that can be obtained by unpickling mpqa_features.pickle.

Licenses for Corpus Content and Annotations

The download site for the MPQA Corpus and annotations states the following licensing terms:

The annotations in this data collection are copyrighted by the MITRE Corporation. User acknowledges and agrees that: (i) as between User and MITRE, MITRE owns all the right, title and interest in the Annotated Content, unless expressly stated otherwise; (ii) nothing in this Agreement shall confer in User any right of ownership in the Annotated Content; and (iii) User is granted a non-exclusive, royalty free, worldwide license (with no right to sublicense) to use the Annotated Content solely for academic and research purposes. This Agreement is governed by the law of the Commonwealth of Massachusetts and User agrees to submit to the exclusive jurisdiction of the Massachusetts courts.

Note: The textual news documents annotated in this corpus have been collected from a wide range of sources and are not copyrighted by the MITRE Corporation. The user acknowledges that the use of these news documents is restricted to research and/or academic purposes only.

The MPQA Subjectivity Lexicon and the MPQA Arguing Lexicon are provided under a GNU General Public License.

.. _MPQA Corpus: http://mpqa.cs.pitt.edu/ .. _MPQA Subjectivity Lexicon: http://mpqa.cs.pitt.edu/lexicons/subj_lexicon/ .. _spaCy: https://honnibal.github.io/spaCy/index.html .. _MPQA Arguing Lexicon: http://mpqa.cs.pitt.edu/lexicons/arg_lexicon/ .. _Wilson, Wiebe, and Hoffmann: http://www.cs.pitt.edu/~wiebe/pubs/papers/emnlp05polarity.pdf .. _download site: http://mpqa.cs.pitt.edu/corpora/mpqa_corpus/ .. _pandas: https://pypi.python.org/pypi/pandas/0.15.2

ms8r / mpqa

readme

Feature Set based on MPQA Corpus

Basic Idea

Data Set

Python Script

Licenses for Corpus Content and Annotations