Topic modeling visualization – How to present the results of LDA models? (2022)

In this post, we discuss techniques to visualize the output and results from topic model (LDA) based on the gensim package.

Topic modeling visualization – How to present the results of LDA models? (1)Topic modeling visualization – How to present the results of LDA models?

Contents

    1. Introduction
    2. Import NewsGroups Dataset
    3. Tokenize Sentences and Clean
    4. Build the Bigram, Trigram Models and Lemmatize
    5. Build the Topic Model

Presenting the Results

  1. What is the Dominant topic and its percentage contribution in each document?
  2. The most representative sentences for each topic
  3. Frequency Distribution of Word Counts in Documents
  4. Word Clouds of Top N Keywords in Each Topic
  5. Word Counts of Topic Keywords
  6. Sentence Chart Colored by Topic
  7. What are the most discussed topics in the documents?
  8. t-SNE Clustering Chart
  9. pyLDAVis
  10. Conclusion

1. Introduction

In topic modeling with gensim, we followed a structured workflow to build an insightful topic model based on the Latent Dirichlet Allocation (LDA) algorithm.

In this post, we will build the topic model using gensim’s native LdaModel and explore multiple strategies to effectively visualize the results using matplotlib plots.

I will be using a portion of the 20 Newsgroups dataset since the focus is more on approaches to visualizing the results.

Let’s begin by importing the packages and the 20 News Groups dataset.

import sys# !{sys.executable} -m spacy download enimport re, numpy as np, pandas as pdfrom pprint import pprint# Gensimimport gensim, spacy, logging, warningsimport gensim.corpora as corporafrom gensim.utils import lemmatize, simple_preprocessfrom gensim.models import CoherenceModelimport matplotlib.pyplot as plt# NLTK Stop wordsfrom nltk.corpus import stopwordsstop_words = stopwords.words('english')stop_words.extend(['from', 'subject', 're', 'edu', 'use', 'not', 'would', 'say', 'could', '_', 'be', 'know', 'good', 'go', 'get', 'do', 'done', 'try', 'many', 'some', 'nice', 'thank', 'think', 'see', 'rather', 'easy', 'easily', 'lot', 'lack', 'make', 'want', 'seem', 'run', 'need', 'even', 'right', 'line', 'even', 'also', 'may', 'take', 'come'])%matplotlib inlinewarnings.filterwarnings("ignore",category=DeprecationWarning)logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.ERROR)

2. Import NewsGroups Dataset

Let’s import the news groups dataset and retain only 4 of the target_names categories.

(Video) LDA Topic Modelling Explained with implementation using gensim in Python #nlp #tutorial

# Import Datasetdf = pd.read_json('https://raw.githubusercontent.com/selva86/datasets/master/newsgroups.json')df = df.loc[df.target_names.isin(['soc.religion.christian', 'rec.sport.hockey', 'talk.politics.mideast', 'rec.motorcycles']) , :]print(df.shape) #> (2361, 3)df.head()

3. Tokenize Sentences and Clean

Removing the emails, new line characters, single quotes and finally split the sentence into a list of words using gensim’s simple_preprocess(). Setting the deacc=True option removes punctuations.

def sent_to_words(sentences): for sent in sentences: sent = re.sub('\S*@\S*\s?', '', sent) # remove emails sent = re.sub('\s+', ' ', sent) # remove newline chars sent = re.sub("\'", "", sent) # remove single quotes sent = gensim.utils.simple_preprocess(str(sent), deacc=True) yield(sent) # Convert to listdata = df.content.values.tolist()data_words = list(sent_to_words(data))print(data_words[:1])# [['from', 'irwin', 'arnstein', 'subject', 're', 'recommendation', 'on', 'duc', 'summary', 'whats', 'it', 'worth', 'distribution', 'usa', 'expires', 'sat', 'may', 'gmt', ...trucated...]]

4. Build the Bigram, Trigram Models and Lemmatize

Let’s form the bigram and trigrams using the Phrases model. This is passed to Phraser() for efficiency in speed of execution.

Next, lemmatize each word to its root form, keeping only nouns, adjectives, verbs and adverbs.

We keep only these POS tags because they are the ones contributing the most to the meaning of the sentences. Here, I use spacy for lemmatization.

# Build the bigram and trigram modelsbigram = gensim.models.Phrases(data_words, min_count=5, threshold=100) # higher threshold fewer phrases.trigram = gensim.models.Phrases(bigram[data_words], threshold=100) bigram_mod = gensim.models.phrases.Phraser(bigram)trigram_mod = gensim.models.phrases.Phraser(trigram)# !python3 -m spacy download en # run in terminal oncedef process_words(texts, stop_words=stop_words, allowed_postags=['NOUN', 'ADJ', 'VERB', 'ADV']): """Remove Stopwords, Form Bigrams, Trigrams and Lemmatization""" texts = [[word for word in simple_preprocess(str(doc)) if word not in stop_words] for doc in texts] texts = [bigram_mod[doc] for doc in texts] texts = [trigram_mod[bigram_mod[doc]] for doc in texts] texts_out = [] nlp = spacy.load('en', disable=['parser', 'ner']) for sent in texts: doc = nlp(" ".join(sent)) texts_out.append([token.lemma_ for token in doc if token.pos_ in allowed_postags]) # remove stopwords once more after lemmatization texts_out = [[word for word in simple_preprocess(str(doc)) if word not in stop_words] for doc in texts_out] return texts_outdata_ready = process_words(data_words) # processed Text Data!

5. Build the Topic Model

To build the LDA topic model using LdaModel(), you need the corpus and the dictionary. Let’s create them first and then build the model. The trained topics (keywords and weights) are printed below as well.

If you examine the topic key words, they are nicely segregate and collectively represent the topics we initially chose: Christianity, Hockey, MidEast and Motorcycles. Nice!

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(Video) How to Create an LDA Topic Model in Python with Gensim (Topic Modeling for DH 03.03)
# Create Dictionaryid2word = corpora.Dictionary(data_ready)# Create Corpus: Term Document Frequencycorpus = [id2word.doc2bow(text) for text in data_ready]# Build LDA modellda_model = gensim.models.ldamodel.LdaModel(corpus=corpus, id2word=id2word, num_topics=4, random_state=100, update_every=1, chunksize=10, passes=10, alpha='symmetric', iterations=100, per_word_topics=True)pprint(lda_model.print_topics())#> [(0,#> '0.017*"write" + 0.015*"people" + 0.014*"organization" + 0.014*"article" + '#> '0.013*"time" + 0.008*"give" + 0.008*"first" + 0.007*"tell" + 0.007*"new" + '#> '0.007*"question"'),#> (1,#> '0.008*"christian" + 0.008*"believe" + 0.007*"god" + 0.007*"law" + '#> '0.006*"state" + 0.006*"israel" + 0.006*"israeli" + 0.005*"exist" + '#> '0.005*"way" + 0.004*"bible"'),#> (2,#> '0.024*"armenian" + 0.012*"bike" + 0.006*"kill" + 0.006*"work" + '#> '0.005*"well" + 0.005*"year" + 0.005*"sumgait" + 0.005*"soldier" + '#> '0.004*"way" + 0.004*"ride"'),#> (3,#> '0.019*"team" + 0.019*"game" + 0.013*"hockey" + 0.010*"player" + '#> '0.009*"play" + 0.009*"win" + 0.009*"nhl" + 0.009*"year" + 0.009*"hawk" + '#> '0.009*"season"')]

6. What is the Dominant topic and its percentage contribution in each document

In LDA models, each document is composed of multiple topics. But, typically only one of the topics is dominant. The below code extracts this dominant topic for each sentence and shows the weight of the topic and the keywords in a nicely formatted output.

This way, you will know which document belongs predominantly to which topic.

def format_topics_sentences(ldamodel=None, corpus=corpus, texts=data): # Init output sent_topics_df = pd.DataFrame() # Get main topic in each document for i, row_list in enumerate(ldamodel[corpus]): row = row_list[0] if ldamodel.per_word_topics else row_list # print(row) row = sorted(row, key=lambda x: (x[1]), reverse=True) # Get the Dominant topic, Perc Contribution and Keywords for each document for j, (topic_num, prop_topic) in enumerate(row): if j == 0: # => dominant topic wp = ldamodel.show_topic(topic_num) topic_keywords = ", ".join([word for word, prop in wp]) sent_topics_df = sent_topics_df.append(pd.Series([int(topic_num), round(prop_topic,4), topic_keywords]), ignore_index=True) else: break sent_topics_df.columns = ['Dominant_Topic', 'Perc_Contribution', 'Topic_Keywords'] # Add original text to the end of the output contents = pd.Series(texts) sent_topics_df = pd.concat([sent_topics_df, contents], axis=1) return(sent_topics_df)df_topic_sents_keywords = format_topics_sentences(ldamodel=lda_model, corpus=corpus, texts=data_ready)# Formatdf_dominant_topic = df_topic_sents_keywords.reset_index()df_dominant_topic.columns = ['Document_No', 'Dominant_Topic', 'Topic_Perc_Contrib', 'Keywords', 'Text']df_dominant_topic.head(10)

7. The most representative sentence for each topic

Sometimes you want to get samples of sentences that most represent a given topic. This code gets the most exemplar sentence for each topic.

# Display setting to show more characters in columnpd.options.display.max_colwidth = 100sent_topics_sorteddf_mallet = pd.DataFrame()sent_topics_outdf_grpd = df_topic_sents_keywords.groupby('Dominant_Topic')for i, grp in sent_topics_outdf_grpd: sent_topics_sorteddf_mallet = pd.concat([sent_topics_sorteddf_mallet, grp.sort_values(['Perc_Contribution'], ascending=False).head(1)], axis=0)# Reset Index sent_topics_sorteddf_mallet.reset_index(drop=True, inplace=True)# Formatsent_topics_sorteddf_mallet.columns = ['Topic_Num', "Topic_Perc_Contrib", "Keywords", "Representative Text"]# Showsent_topics_sorteddf_mallet.head(10)

8. Frequency Distribution of Word Counts in Documents

When working with a large number of documents, you want to know how big the documents are as a whole and by topic. Let’s plot the document word counts distribution.

(Video) LDAvis: A method for visualizing and interpreting topic models

doc_lens = [len(d) for d in df_dominant_topic.Text]# Plotplt.figure(figsize=(16,7), dpi=160)plt.hist(doc_lens, bins = 1000, color='navy')plt.text(750, 100, "Mean : " + str(round(np.mean(doc_lens))))plt.text(750, 90, "Median : " + str(round(np.median(doc_lens))))plt.text(750, 80, "Stdev : " + str(round(np.std(doc_lens))))plt.text(750, 70, "1%ile : " + str(round(np.quantile(doc_lens, q=0.01))))plt.text(750, 60, "99%ile : " + str(round(np.quantile(doc_lens, q=0.99))))plt.gca().set(xlim=(0, 1000), ylabel='Number of Documents', xlabel='Document Word Count')plt.tick_params(size=16)plt.xticks(np.linspace(0,1000,9))plt.title('Distribution of Document Word Counts', fontdict=dict(size=22))plt.show()

import seaborn as snsimport matplotlib.colors as mcolorscols = [color for name, color in mcolors.TABLEAU_COLORS.items()] # more colors: 'mcolors.XKCD_COLORS'fig, axes = plt.subplots(2,2,figsize=(16,14), dpi=160, sharex=True, sharey=True)for i, ax in enumerate(axes.flatten()): df_dominant_topic_sub = df_dominant_topic.loc[df_dominant_topic.Dominant_Topic == i, :] doc_lens = [len(d) for d in df_dominant_topic_sub.Text] ax.hist(doc_lens, bins = 1000, color=cols[i]) ax.tick_params(axis='y', labelcolor=cols[i], color=cols[i]) sns.kdeplot(doc_lens, color="black", shade=False, ax=ax.twinx()) ax.set(xlim=(0, 1000), xlabel='Document Word Count') ax.set_ylabel('Number of Documents', color=cols[i]) ax.set_title('Topic: '+str(i), fontdict=dict(size=16, color=cols[i]))fig.tight_layout()fig.subplots_adjust(top=0.90)plt.xticks(np.linspace(0,1000,9))fig.suptitle('Distribution of Document Word Counts by Dominant Topic', fontsize=22)plt.show()

9. Word Clouds of Top N Keywords in Each Topic

Though you’ve already seen what are the topic keywords in each topic, a word cloud with the size of the words proportional to the weight is a pleasant sight. The coloring of the topics I’ve taken here is followed in the subsequent plots as well.

# 1. Wordcloud of Top N words in each topicfrom matplotlib import pyplot as pltfrom wordcloud import WordCloud, STOPWORDSimport matplotlib.colors as mcolorscols = [color for name, color in mcolors.TABLEAU_COLORS.items()] # more colors: 'mcolors.XKCD_COLORS'cloud = WordCloud(stopwords=stop_words, background_color='white', width=2500, height=1800, max_words=10, colormap='tab10', color_func=lambda *args, **kwargs: cols[i], prefer_horizontal=1.0)topics = lda_model.show_topics(formatted=False)fig, axes = plt.subplots(2, 2, figsize=(10,10), sharex=True, sharey=True)for i, ax in enumerate(axes.flatten()): fig.add_subplot(ax) topic_words = dict(topics[i][1]) cloud.generate_from_frequencies(topic_words, max_font_size=300) plt.gca().imshow(cloud) plt.gca().set_title('Topic ' + str(i), fontdict=dict(size=16)) plt.gca().axis('off')plt.subplots_adjust(wspace=0, hspace=0)plt.axis('off')plt.margins(x=0, y=0)plt.tight_layout()plt.show()

10. Word Counts of Topic Keywords

When it comes to the keywords in the topics, the importance (weights) of the keywords matters. Along with that, how frequently the words have appeared in the documents is also interesting to look.

Let’s plot the word counts and the weights of each keyword in the same chart.

You want to keep an eye out on the words that occur in multiple topics and the ones whose relative frequency is more than the weight. Often such words turn out to be less important. The chart I’ve drawn below is a result of adding several such words to the stop words list in the beginning and re-running the training process.

from collections import Countertopics = lda_model.show_topics(formatted=False)data_flat = [w for w_list in data_ready for w in w_list]counter = Counter(data_flat)out = []for i, topic in topics: for word, weight in topic: out.append([word, i , weight, counter[word]])df = pd.DataFrame(out, columns=['word', 'topic_id', 'importance', 'word_count']) # Plot Word Count and Weights of Topic Keywordsfig, axes = plt.subplots(2, 2, figsize=(16,10), sharey=True, dpi=160)cols = [color for name, color in mcolors.TABLEAU_COLORS.items()]for i, ax in enumerate(axes.flatten()): ax.bar(x='word', height="word_count", data=df.loc[df.topic_id==i, :], color=cols[i], width=0.5, alpha=0.3, label='Word Count') ax_twin = ax.twinx() ax_twin.bar(x='word', height="importance", data=df.loc[df.topic_id==i, :], color=cols[i], width=0.2, label='Weights') ax.set_ylabel('Word Count', color=cols[i]) ax_twin.set_ylim(0, 0.030); ax.set_ylim(0, 3500) ax.set_title('Topic: ' + str(i), color=cols[i], fontsize=16) ax.tick_params(axis='y', left=False) ax.set_xticklabels(df.loc[df.topic_id==i, 'word'], rotation=30, horizontalalignment= 'right') ax.legend(loc='upper left'); ax_twin.legend(loc='upper right')fig.tight_layout(w_pad=2) fig.suptitle('Word Count and Importance of Topic Keywords', fontsize=22, y=1.05) plt.show()

11. Sentence Chart Colored by Topic

Each word in the document is representative of one of the 4 topics. Let’s color each word in the given documents by the topic id it is attributed to.
The color of the enclosing rectangle is the topic assigned to the document.

(Video) LDA Topic modeling in R

# Sentence Coloring of N Sentencesfrom matplotlib.patches import Rectangledef sentences_chart(lda_model=lda_model, corpus=corpus, start = 0, end = 13): corp = corpus[start:end] mycolors = [color for name, color in mcolors.TABLEAU_COLORS.items()] fig, axes = plt.subplots(end-start, 1, figsize=(20, (end-start)*0.95), dpi=160) axes[0].axis('off') for i, ax in enumerate(axes): if i > 0: corp_cur = corp[i-1] topic_percs, wordid_topics, wordid_phivalues = lda_model[corp_cur] word_dominanttopic = [(lda_model.id2word[wd], topic[0]) for wd, topic in wordid_topics] ax.text(0.01, 0.5, "Doc " + str(i-1) + ": ", verticalalignment='center', fontsize=16, color='black', transform=ax.transAxes, fontweight=700) # Draw Rectange topic_percs_sorted = sorted(topic_percs, key=lambda x: (x[1]), reverse=True) ax.add_patch(Rectangle((0.0, 0.05), 0.99, 0.90, fill=None, alpha=1, color=mycolors[topic_percs_sorted[0][0]], linewidth=2)) word_pos = 0.06 for j, (word, topics) in enumerate(word_dominanttopic): if j < 14: ax.text(word_pos, 0.5, word, horizontalalignment='left', verticalalignment='center', fontsize=16, color=mycolors[topics], transform=ax.transAxes, fontweight=700) word_pos += .009 * len(word) # to move the word for the next iter ax.axis('off') ax.text(word_pos, 0.5, '. . .', horizontalalignment='left', verticalalignment='center', fontsize=16, color='black', transform=ax.transAxes) plt.subplots_adjust(wspace=0, hspace=0) plt.suptitle('Sentence Topic Coloring for Documents: ' + str(start) + ' to ' + str(end-2), fontsize=22, y=0.95, fontweight=700) plt.tight_layout() plt.show()sentences_chart() 

12. What are the most discussed topics in the documents?

Let’s compute the total number of documents attributed to each topic.

# Sentence Coloring of N Sentencesdef topics_per_document(model, corpus, start=0, end=1): corpus_sel = corpus[start:end] dominant_topics = [] topic_percentages = [] for i, corp in enumerate(corpus_sel): topic_percs, wordid_topics, wordid_phivalues = model[corp] dominant_topic = sorted(topic_percs, key = lambda x: x[1], reverse=True)[0][0] dominant_topics.append((i, dominant_topic)) topic_percentages.append(topic_percs) return(dominant_topics, topic_percentages)dominant_topics, topic_percentages = topics_per_document(model=lda_model, corpus=corpus, end=-1) # Distribution of Dominant Topics in Each Documentdf = pd.DataFrame(dominant_topics, columns=['Document_Id', 'Dominant_Topic'])dominant_topic_in_each_doc = df.groupby('Dominant_Topic').size()df_dominant_topic_in_each_doc = dominant_topic_in_each_doc.to_frame(name='count').reset_index()# Total Topic Distribution by actual weighttopic_weightage_by_doc = pd.DataFrame([dict(t) for t in topic_percentages])df_topic_weightage_by_doc = topic_weightage_by_doc.sum().to_frame(name='count').reset_index()# Top 3 Keywords for each Topictopic_top3words = [(i, topic) for i, topics in lda_model.show_topics(formatted=False) for j, (topic, wt) in enumerate(topics) if j < 3]df_top3words_stacked = pd.DataFrame(topic_top3words, columns=['topic_id', 'words'])df_top3words = df_top3words_stacked.groupby('topic_id').agg(', \n'.join)df_top3words.reset_index(level=0,inplace=True)

Let’s make two plots:

  1. The number of documents for each topic by assigning the document to the topic that has the most weight in that document.
  2. The number of documents for each topic by by summing up the actual weight contribution of each topic to respective documents.
from matplotlib.ticker import FuncFormatter# Plotfig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 4), dpi=120, sharey=True)# Topic Distribution by Dominant Topicsax1.bar(x='Dominant_Topic', height='count', data=df_dominant_topic_in_each_doc, width=.5, color='firebrick')ax1.set_xticks(range(df_dominant_topic_in_each_doc.Dominant_Topic.unique().__len__()))tick_formatter = FuncFormatter(lambda x, pos: 'Topic ' + str(x)+ '\n' + df_top3words.loc[df_top3words.topic_id==x, 'words'].values[0])ax1.xaxis.set_major_formatter(tick_formatter)ax1.set_title('Number of Documents by Dominant Topic', fontdict=dict(size=10))ax1.set_ylabel('Number of Documents')ax1.set_ylim(0, 1000)# Topic Distribution by Topic Weightsax2.bar(x='index', height='count', data=df_topic_weightage_by_doc, width=.5, color='steelblue')ax2.set_xticks(range(df_topic_weightage_by_doc.index.unique().__len__()))ax2.xaxis.set_major_formatter(tick_formatter)ax2.set_title('Number of Documents by Topic Weightage', fontdict=dict(size=10))plt.show()

13. t-SNE Clustering Chart

Let’s visualize the clusters of documents in a 2D space using t-SNE (t-distributed stochastic neighbor embedding) algorithm.

# Get topic weights and dominant topics ------------from sklearn.manifold import TSNEfrom bokeh.plotting import figure, output_file, showfrom bokeh.models import Labelfrom bokeh.io import output_notebook# Get topic weightstopic_weights = []for i, row_list in enumerate(lda_model[corpus]): topic_weights.append([w for i, w in row_list[0]])# Array of topic weights arr = pd.DataFrame(topic_weights).fillna(0).values# Keep the well separated points (optional)arr = arr[np.amax(arr, axis=1) > 0.35]# Dominant topic number in each doctopic_num = np.argmax(arr, axis=1)# tSNE Dimension Reductiontsne_model = TSNE(n_components=2, verbose=1, random_state=0, angle=.99, init='pca')tsne_lda = tsne_model.fit_transform(arr)# Plot the Topic Clusters using Bokehoutput_notebook()n_topics = 4mycolors = np.array([color for name, color in mcolors.TABLEAU_COLORS.items()])plot = figure(title="t-SNE Clustering of {} LDA Topics".format(n_topics), plot_width=900, plot_height=700)plot.scatter(x=tsne_lda[:,0], y=tsne_lda[:,1], color=mycolors[topic_num])show(plot)

14. pyLDAVis

Finally, pyLDAVis is the most commonly used and a nice way to visualise the information contained in a topic model. Below is the implementation for LdaModel().

import pyLDAvis.gensimpyLDAvis.enable_notebook()vis = pyLDAvis.gensim.prepare(lda_model, corpus, dictionary=lda_model.id2word)vis

15. Conclusion

We started from scratch by importing, cleaning and processing the newsgroups dataset to build the LDA model. Then we saw multiple ways to visualize the outputs of topic models including the word clouds and sentence coloring, which intuitively tells you what topic is dominant in each topic. A t-SNE clustering and the pyLDAVis are provide more details into the clustering of the topics.

(Video) Tutorial on topic modelling in r tutorial

Where next? If you are familiar with scikit learn, you can build and grid search topic models using scikit learn as well.

FAQs

How do you evaluate LDA? ›

LDA is typically evaluated by either measuring perfor- mance on some secondary task, such as document clas- sification or information retrieval, or by estimating the probability of unseen held-out documents given some training documents.

How does LDA Work For topic models? ›

LDA operates in the same way as PCA does. LDA is applied to the text data. It works by decomposing the corpus document word matrix (the larger matrix) into two parts (smaller matrices): the Document Topic Matrix and the Topic Word. Therefore, LDA like PCA is a matrix factorization technique.

What is the output of topic Modelling? ›

The output of the algorithm is a vector that contains the coverage of every topic for the document being modeled. It will look something like this [0.2, 0.5, etc.]

How do I know how many topics in LDA? ›

To decide on a suitable number of topics, you can compare the goodness-of-fit of LDA models fit with varying numbers of topics. You can evaluate the goodness-of-fit of an LDA model by calculating the perplexity of a held-out set of documents. The perplexity indicates how well the model describes a set of documents.

How do you validate a topic model? ›

The whole process consists of 4 main steps: (1) a standard text processing block to get the bag of words representation of each document, (2) topic modeling, to project the documents onto a semantic space, (3) semantic graph computation, and (4) model validation using the two metrics proposed in the previous ...

What is an LDA score? ›

Linear Discriminant Analysis (LDA) scores of differentially abundant species among individuals who consume coffee (green) or not (red). The LDA scores represent the effect size of each abundant species. Species enriched in each group with an LDA score >2 are considered. Source publication.

What is the purpose of topic Modelling? ›

The aim of topic modeling is to discover the themes that run through a corpus by analyzing the words of the original texts.

What are assumptions imposed on LDA? ›

LDA assumes that each input variable has the same variance. It is almost always a good idea to standardize your data before using LDA so that it has a mean of 0 and a standard deviation of 1.

What is the output of LDA Model? ›

LDA ( short for Latent Dirichlet Allocation ) is an unsupervised machine-learning model that takes documents as input and finds topics as output. The model also says in what percentage each document talks about each topic. A topic is represented as a weighted list of words.

What is LDA in text analysis? ›

Latent Dirichlet Allocation (LDA) is an example of topic model and is used to classify text in a document to a particular topic. It builds a topic per document model and words per topic model, modeled as Dirichlet distributions.

What input do you give to a topic model? ›

The two main inputs to the LDA topic model are the dictionary( id2word ) and the corpus. Let's create them. Gensim creates a unique id for each word in the document.

How do you know if a topic model is accurate? ›

There are a number of ways to evaluate topic models, including:
  1. Human judgment. Observation-based, eg. observing the top 'n' words in a topic. ...
  2. Quantitative metrics – Perplexity (held out likelihood) and coherence calculations.
  3. Mixed approaches – Combinations of judgment-based and quantitative approaches.

What is a good topic coherence score? ›

There is no one way to determine whether the coherence score is good or bad. The score and its value depend on the data that it's calculated from. For instance, in one case, the score of 0.5 might be good enough but in another case not acceptable. The only rule is that we want to maximize this score.

What is the optimal number of topics for LDA in Python? ›

How to find optimum number of topics ? One approach to find optimum number of topics is build many LDA models with different values of number of topics and pick the one that gives highest coherence value. If you see the same keywords being repeated in multiple topics, it's probably a sign that the 'k' is too large.

What is perplexity score LDA? ›

Perplexity is a statistical measure of how well a probability model predicts a sample. As applied to LDA, for a given value of , you estimate the LDA model. Then given the theoretical word distributions represented by the topics, compare that to the actual topic mixtures, or distribution of words in your documents.

What is ETA in LDA? ›

Understanding the role of LDA model hyperparameters

alpha is a parameter that controls the prior distribution over topic weights in each document, while eta is a parameter for the prior distribution over word weights in each topic. In gensim , both default to a symmetric, 1 / num_topics prior.

How will you decide the topic of the corpus? ›

To compute topic coherence of a topic model, we perform the following steps.
  • Select the top n frequently occurring words in each topic.
  • Compute pairwise scores (UCI or UMass) for each of the words selected above and aggregate all the pairwise scores to calculate the coherence score for a particular topic.
16 Apr 2018

Why do we use linear discriminant analysis? ›

Linear discriminant analysis is primarily used here to reduce the number of features to a more manageable number before classification. Each of the new dimensions is a linear combination of pixel values, which form a template.

Why LDA is used? ›

Linear discriminant analysis (LDA) is used here to reduce the number of features to a more manageable number before the process of classification. Each of the new dimensions generated is a linear combination of pixel values, which form a template.

What is the reason for carrying out LDA? ›

The goal of LDA is to project the features in higher dimensional space onto a lower-dimensional space in order to avoid the curse of dimensionality and also reduce resources and dimensional costs.

What is LDA effect size? ›

Linear discriminant analysis (LDA) effect size (LEfSe) effect size was used to calculate the taxa that best discriminated between the synbiotic and control groups. (a) Expressed in a cladogram, taxa that reached a linear discriminant analysis score (log10) >2.0 are highlighted and labelled accordingly.

What is negative LDA score? ›

A negative coefficient would be interpreted as indicating that, when the other IVs are held constant, and increase in the IV of interest would mean that the discriminant function score for a case is predicted to decrease.

What is the maximum number of discriminant vectors that can be produced by LDA? ›

LDA produces at most c − 1 discriminant vectors.

Where is topic modeling used? ›

Topic Models are very useful for the purpose for document clustering, organizing large blocks of textual data, information retrieval from unstructured text and feature selection. For Example – New York Times are using topic models to boost their user – article recommendation engines.

What is topic model analysis? ›

Topic analysis (also called topic detection, topic modeling, or topic extraction) is a machine learning technique that organizes and understands large collections of text data, by assigning “tags” or categories according to each individual text's topic or theme.

How do you prepare data for linear discriminant analysis? ›

LDA in 5 steps
  1. Step 1: Computing the d-dimensional mean vectors. ...
  2. Step 2: Computing the Scatter Matrices. ...
  3. Step 3: Solving the generalized eigenvalue problem for the matrix S−1WSB. ...
  4. Step 4: Selecting linear discriminants for the new feature subspace.
3 Aug 2014

What are limitations of linear discriminant analysis? ›

Linear Discriminant Analysis (LDA) :

It still beats some algorithms (logistic regression) when its assumptions are met. Cons : a) It requires normal distribution assumption on features/predictors. b) Sometimes not good for few categories variables.

Is LDA generative or discriminative? ›

LDA is a generative model because it uses the joint probability distribution, P(x,y).

What is the goal of LDA? ›

The aim of LDA is to maximize the between-class variance and minimize the within-class variance, through a linear discriminant function, under the assumption that data in every class are described by a Gaussian probability density function with the same covariance.

What is a good coherence score? ›

Coherence Score Guide________ 0.5 basic – good beginner level 1.0 good 2.0 very good 3.0+ excellent .

Can I use LDA for classification? ›

LDA supports both binary and multi-class classification. Gaussian Distribution. The standard implementation of the model assumes a Gaussian distribution of the input variables.

What is a LDA model? ›

LDA is a three-level hierarchical Bayesian model, in which each item of a collection is modeled as a finite mixture over an underlying set of topics. Each topic is, in turn, modeled as an infinite mixture over an underlying set of topic probabilities.

How does LDA prepare data? ›

LDA in 5 steps
  1. Step 1: Computing the d-dimensional mean vectors. ...
  2. Step 2: Computing the Scatter Matrices. ...
  3. Step 3: Solving the generalized eigenvalue problem for the matrix S−1WSB. ...
  4. Step 4: Selecting linear discriminants for the new feature subspace.
3 Aug 2014

How does LDA reduce dimensionality? ›

Specifically, you learned: Dimensionality reduction involves reducing the number of input variables or columns in modeling data. LDA is a technique for multi-class classification that can be used to automatically perform dimensionality reduction.

What is LDA feature selection? ›

LDA is a dimensionality reduction technique which reduces the dimension of the data. Feature selection is the process of selecting a set of features from the entire set of features. In feature selection as well, we are reducing the dimension of the data.

What is best coherence score for LDA? ›

achieve the highest coherence score = 0.4495 when the number of topics is 2 for LSA, for NMF the highest coherence value is 0.6433 for K = 4, and for LDA we also get number of topics is 4 with the highest coherence score which is 0.3871 (see Fig. ... ...

What is coherence score in LDA model? ›

Coherence Measures

C_uci measure is based on a sliding window and the pointwise mutual information (PMI) of all word pairs of the given top words. C_umass is based on document cooccurrence counts, a one-preceding segmentation and a logarithmic conditional probability as confirmation measure.

What is perplexity score LDA? ›

Perplexity is a statistical measure of how well a probability model predicts a sample. As applied to LDA, for a given value of , you estimate the LDA model. Then given the theoretical word distributions represented by the topics, compare that to the actual topic mixtures, or distribution of words in your documents.

How does LDA measure accuracy? ›

The overall accuracy is estimated as 0.755. If however we keep the same rule, but change the prior proportions of the two classes, the overall accuracy will change. If for example, the two classes are in the ratio 0.9:0.1, the overall accuracy will be 0.9 × 0.8636 + 0.1 × 0.5441 ≃ 0.83.

What is discriminant score in LDA? ›

Linear discriminant analysis (LDA), normal discriminant analysis (NDA), or discriminant function analysis is a generalization of Fisher's linear discriminant, a method used in statistics and other fields, to find a linear combination of features that characterizes or separates two or more classes of objects or events.

How many discriminant vectors can LDA produce? ›

LDA produces at most c − 1 discriminant vectors.

Why LDA is generative model? ›

LDA is a generative model because it uses the joint probability distribution, P(x,y).

How does a topic model work? ›

Topic modelling is done using LDA(Latent Dirichlet Allocation). Topic modelling refers to the task of identifying topics that best describes a set of documents. These topics will only emerge during the topic modelling process (therefore called latent).

Videos

1. LDA Topic Models
(Andrius Knispelis)
2. Practical Python for DH: Topic Modeling Visualization
(pvierth)
3. Topic Modelling | Latent Dirichlet Allocation in Python | LDA in Python
(Analytics Excellence)
4. Topic Modeling Workshop for the Beginners in Python
(Prodramp)
5. DASS: Topic modelling
(Jos Elkink)
6. End to End Topic Modeling with BERTopic
(Zoum datascience)

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