def lda_model_evaluation():

    """
    This function loops through a number of parameters for an LDA model, creates the model,
    computes the coherenece score, and saves the results in a pandas dataframe. The outputed dataframe
    contains the values of the parameters tested and the resulting coherence score.
    """

    #define empty lists to save results
    topic_number, decay_rate_list, score  = [], [], []

    #loop through a number of parameters
    for topics in range(5,12):
        for decay_rate in [0.5, 0.6, 0.7]:

                #build LDA model
                lda_model = LdaModel(corpus = corpus, id2word = id2word, num_topics = topics, decay = decay_rate,
                               random_state = 0, chunksize = 100, alpha = 'auto', per_word_topics = True)

                #compute coherence score
                coherence_model_lda = CoherenceModel(model = lda_model, texts = texts, dictionary = id2word, coherence = 'c_v')
                coherence_score = coherence_model_lda.get_coherence()

                #append parameters to lists
                topic_number.append(topics)
                decay_rate_list.append(decay_rate)
                score.append(coherence_score)

                print("Model Saved")

    #gather result into a dataframe
    results = {"Number of Topics": topic_number,
                "Decay Rate": decay_rate_list,
                "Score": score}

    results = pd.DataFrame(results)

    return(results)