def word_similarity_explorer_gensim(corpus,
                                    category,
                                    target_term,
                                    category_name=None,
                                    not_category_name=None,
                                    word2vec=None,
                                    alpha=0.01,
                                    max_p_val=0.1,
                                    term_significance=None,
                                    **kwargs):
    '''
        Parameters
        ----------
        corpus : Corpus
            Corpus to use.
        category : str
            Name of category column as it appears in original data frame.
        category_name : str
            Name of category to use.  E.g., "5-star reviews."
        not_category_name : str
            Name of everything that isn't in category.  E.g., "Below 5-star reviews".
        target_term : str
            Word or phrase for semantic similarity comparison
        word2vec : word2vec.Word2Vec
          Gensim-compatible Word2Vec model of lower-cased corpus. If none, o
          ne will be trained using Word2VecFromParsedCorpus(corpus).train()
        alpha : float, default = 0.01
            Uniform dirichlet prior for p-value calculation
        max_p_val : float, default = 0.1
            Max p-val to use find set of terms for similarity calculation
        term_significance : TermSignificance
            Significance finder

        Remaining arguments are from `produce_scattertext_explorer`.
        Returns
        -------
            str, html of visualization
        '''

    if word2vec is None:
        word2vec = Word2VecFromParsedCorpus(corpus).train()

    if term_significance is None:
        term_significance = LogOddsRatioUninformativeDirichletPrior(alpha)
    assert issubclass(type(term_significance), TermSignificance)

    scores = []

    for tok in corpus._term_idx_store._i2val:
        try:
            scores.append(word2vec.similarity(target_term, tok.replace(' ', '_')))
        except:
            try:
                scores.append(np.mean([word2vec.similarity(target_term, tok_part)
                                       for tok_part in tok.split()]))
            except:
                scores.append(0)
    scores = np.array(scores)

    return produce_scattertext_explorer(corpus,
                                        category,
                                        category_name,
                                        not_category_name,
                                        scores=scores,
                                        sort_by_dist=False,
                                        reverse_sort_scores_for_not_category=False,
                                        word_vec_use_p_vals=True,
                                        term_significance=term_significance,
                                        max_p_val=max_p_val,
                                        p_value_colors=True,
                                        **kwargs)
Parameters
        ----------
        corpus : Corpus
            Corpus to use.
        category : str
            Name of category column as it appears in original data frame.
        category_name : str
            Name of category to use.  E.g., "5-star reviews."
        not_category_name : str
            Name of everything that isn't in category.  E.g., "Below 5-star reviews".
        target_term : str
            Word or phrase for semantic similarity comparison
        word2vec : word2vec.Word2Vec
          Gensim-compatible Word2Vec model of lower-cased corpus. If none, o
          ne will be trained using Word2VecFromParsedCorpus(corpus).train()
        alpha : float, default = 0.01
            Uniform dirichlet prior for p-value calculation
        max_p_val : float, default = 0.1
            Max p-val to use find set of terms for similarity calculation
        term_significance : TermSignificance
            Significance finder

        Remaining arguments are from `produce_scattertext_explorer`.
        Returns
        -------
            str, html of visualization