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Stance Detection and Stance Classification

Authors: Xiaochi (George) Li, Xiaodan Chen
Capstone project of Data Science master's program at the George Washington University
Read our presentation in presentation and report
Watch Presentation on Youtube Presentation on Youtube

My contribution:
Major contributor (account for >70% of the contribution)
Design the framework of the project, lead another intern during the project. 
Data loading, relabeling, stance classification, EDA, visualization, deployment and writing major part of presentation, report, documentation.

Usage

Environment: Anaconda, Spacy, Gensim, Keras, nltk

NOTE: Please only read/use the code in ./deployment, code in other folders are legacy of previous development, and may not run as expected.
Clone this repository to local machine. Change current directory to ./deployment

Usage Example:

from stance_detection_classification import StanceDetectionAndClassification
sdac = StanceDetectionAndClassification()
sd = sdac.stance_detection(*string*) # sd is the probability that the speech contain stance
sc = sdac.stance_classification(*string*)  # sc is the probability that the speech contain positive stance

Run Unit test: > python3 test.py

class StanceDetectionAndClassification
Methods:

  • __init__(self, data_path: str = '../opinion_mining/')
    Define the paths of data to retrain and trained model
    Parameter: data_path: optional, the path to the data to retrain the models.

  • retrain_stance_detection(self)
    Retrain the stance detection model

  • retrain_stance_classification(self)
    Retrain the stance classification model

  • stance_detection(self, speech: str) Stance Detection Model
    Parameter: speech(string)
    Return: float, the probability of the speech contain stance in it

  • stance_classification(self, speech: str) Stance Classification Model
    Parameter: speech(string)
    Return: float, the probability of the speech contain positive

Models:

  • Stance Detection: pre-trained GloVe + LSTM
  • Stance Classification: Remove stop words+ tfidf + smote + Logistic Regression

Underlying Components in util_code

module util_code.corpus_loader
Useful to get speech from XML files, dependent on util_code.xml_parser

  • Function: corpus_loader(debug=False, parser='bs', data_root='../opinion_mining/')
    Match the record between action.csv and document.csv and load corpus from XML

    Parameters:

    • debug(Bool): the switch for debug
    • parser(str): which parser to use, bs/xml
    • data_root(str): the root of data and labels

    Returns:

    • Pandas DataFrame

  • Function: untagged_corpus_loader(tagged_df=None, path_root='../opinion_mining')
    Load all the untagged corpus from XML files

    Parameters:

    • tagged_df(Pandas DataFrame): the tagged data frame
    • path_root(str): the root of the path to search all XML files

    Returns:

    • untagged_data_frame(Pandas DataFrame): untagged data frame, in the same format of tagged_df

class util_code.Pipeline_V1.Pipeline
The one-stop pipeline for training, evaluating and diagnosing any sklearn model

Attributes

  • model : trained model
  • vectorizer: trained vectorizer

Methods:

  • __init__(self, x, y, vectorizer, model, silent=False, sampler=None)
    Define the components of pipeline Parameters:
    • vectorizer: sklearn style vectorizer, has fit, transform methods
    • model: sklearn style model, has fit, predict, predict_proba methods
    • silent: Deprecated, please use in exec()
    • sampler: imblearn sampler, has fit_sample method
  • model_evaluation(self)
    Model evaluation, print confusion matrix, Log loss, classification report and Precision-Recall Plot
  • exec(self, silent=False)
    Run pipeline
    Parameter: silent: bool, if True then return the F1 score as dictionary. If False then return the model and print the evaluations.

class util_code.mean_embedding_vectorizer.StackedMeanEmbeddingVectorizer
Stack mean embedding of Word2Vec to Bag of Words embedding
Implemented in sklearn style

Methods:

  • __init__(self, vectorizer=None)
    Parameter: vectorizer: sklearn style vectorizer
  • load(self, file)
    Parameter: file(str): path to pre-trained Word2Vec model
  • fit(self, X)
    Parameter: X(one numpy column contain str), same to sklearn.vectorizer
    Return: scipy.sparse: n_sample*n_features
  • transform(self, X)
    Parameter: X(one numpy column contain str), same to sklearn.vectorizer
    Return: scipy.sparse: n_sample*n_features

class util_code.doc2vec_vectorizer.StackedD2V
Stack Doc2Vec embedding to Bag of Words embedding
Implemented in sklearn style

Methods:

  • __init__(self, file, vectorizer=None)
    Parameter:
    • file(str): trained Doc2Vec model
    • vectorizer: sklearn style vectorizer
  • load(self, file)
    Parameter: file(str): path to pre-trained Word2Vec model
  • fit(self, X)
    Parameter: X(one numpy column contain str), same to sklearn.vectorizer
    Return: scipy.sparse: n_sample*n_features
  • transform(self, X)
    Parameter: X(one numpy column contain str), same to sklearn.vectorizer
    Return: scipy.sparse: n_sample*n_features

class util_code.Regex_Stance_Detection.RuleBasedStanceDetection

Methods:

  • stance_detection_labeler(self, speech, strict=True, pn_ratio=1, cutoff=None)
    Parameters:

    • speech(str): the speech
    • strict(bool): only label as contain stance when detect positive or negative words.
    • pn_ratio(int,float): the parameter to control the weight of negative keywords when both positive and negative keywords appear in the speech
    • cutoff(int): the cutoff point of the speech, only detect the keyword before cutoff

    Return: int 1:contain stance, -1: not contain stance

  • stance_classification_labeler(self, speech, pn_ratio=1, cutoff=None) Parameters:

    • speech(str): the speech
    • pn_ratio(int,float): the parameter to control the weight of negative keywords when both positive and negative keywords appear in the speech
    • cutoff(int): the cutoff point of the speech, only detect the keyword before cutoff

    Return: int 1:contain positive stance, -1: contain negative stance

module util_code.preprocess_utility

Functions:

  • spacy_lemma(speech:str) -> str
    Lemmatization
  • remove_stopwords(text:str, cutoff:int=10000) -> str
    Remove stop words, limit word in the sentence to cutoff
  • remove_stopwords_return_list(text, cutoff=10000) -> List[str] Remove stop words, limit word in the sentence to cutoff, return list instead of combined string

module util_code.parallel_computing

Functions:

  • parallel_remove_stopwords(x:array of str) -> List[str]
    Remove stop words, paralleled by python's multiprocessing library
Xiaodan's contribution in util_code

module util_code.data_preprocessing

text preprocessing methods

  • tokenize_text

    • tokenization

  • remove_stopwords

  • remove_special_characters

    • remove special characters --> '!"#$%&'()*+,-./:;<=>?@[\]^_`{|}'

  • remove_non_alphabetic_characters

    • remove non-alphabetic characters and numbers

  • remove_tokens_with_length

    • remove tokens with length less than or equal the input length

  • get_common_tokens

    • get the vocabulary of the corpus

  • relabel_data

    • using relabel algorithm to relabel untagged speeches

  • change_labels

    • relabel '-1' to '0' for labels for later deep learning model

  • split

    • a combination of text preprocessing, data relabeling and data splitting

    Parameters

    • min_speech_len:the maximum word count you use to control word frequency in a speech
    • max_speech_len:the minimum word count you use to control word frequency in a speech
    • max_wc: maintain word tokens that appear in the whole corpus that are less than max_wc words
    • min_wc: maintain word tokens that appear in the whole corpus that are more than min_wc words

  • get_fixed_length_range_data

    • remove speeches whose length is shorter than min_len or longer than max_len

  • clean_corpus

    • ensure all speeches in a corpus only keep tokens between a minimum occurence and a maximum ocurrence

module util_code.lstm_train LSTM Model for stance detection

  • keras_tokenizer

    • tokenization

  • glove_embedding

    • load pretrained GloVe embedding

  • LSTM_model

    • define model

  • prediction

    • get f1 score on test data

  • plot_history

    • visualization for loss and accuracy

  • train

    • train model, save model and tokenizer

module util_code.sd_train

a concise version of training model for stance detection

  • Parameters
    • input_len: input length of speech to feed the deep learning
    • save_model_path : the path to the pretrained model
    • glove_path : the path to the pretrained GloVe embedding
    • tokenizer_path : the path to the pretrained vectorization tokenizer

module util_code.sd_evaluation

get prediction and evaluation

class util_code.sd_prediction

single speech prediction