{ "cells": [ { "attachments": {}, "cell_type": "markdown", "id": "8f5d9cac", "metadata": { "slideshow": { "slide_type": "slide" }, "tags": [] }, "source": [ "# Lab: Topic Coherence\n", "\n", "In this section, we will evaluate some topics using the `20newsgroups` dataset as a reference corpus to illustrate topic coherence in practice. We will first preprocess the data and then create some random topics. Finally, we will compute the coherence scores for these topics using Gensim's CoherenceModel.\n" ] }, { "cell_type": "code", "execution_count": 2, "id": "99b2a449", "metadata": {}, "outputs": [], "source": [ "%config InlineBackend.figure_format='retina'\n", "\n", "import re\n", "import pandas as pd\n", "import seaborn as sns\n", "import matplotlib.pyplot as plt\n", "from sklearn.datasets import fetch_20newsgroups\n", "from nltk.corpus import stopwords\n", "from nltk.collocations import BigramCollocationFinder, BigramAssocMeasures\n", "from gensim.models.coherencemodel import CoherenceModel\n", "from gensim.corpora.dictionary import Dictionary" ] }, { "attachments": {}, "cell_type": "markdown", "id": "80df897d", "metadata": {}, "source": [ "## Loading the Data\n", "\n", "We first load and preprocess the `20newsgroups` dataset by removing headers, footers, and quotes, tokenizing the texts, and removing stopwords.\n" ] }, { "cell_type": "code", "execution_count": 3, "id": "8f866d41", "metadata": { "slideshow": { "slide_type": "slide" }, "tags": [] }, "outputs": [], "source": [ "# Load the 20newsgroups dataset and preprocess the texts\n", "texts, _ = fetch_20newsgroups(\n", " subset=\"all\", remove=(\"headers\", \"footers\", \"quotes\"), return_X_y=True\n", ")\n", "tokenizer = lambda s: re.findall(\"\\w+\", s.lower())\n", "texts = [tokenizer(t) for t in texts]\n", "stopwords = set(stopwords.words(\"english\"))\n", "texts = [[w for w in t if w not in stopwords and len(w) > 1] for t in texts]" ] }, { "attachments": {}, "cell_type": "markdown", "id": "df13391d", "metadata": {}, "source": [ "Then, we find collocations in the texts and display the top 10 collocations.\n" ] }, { "cell_type": "code", "execution_count": 4, "id": "1a596b97", "metadata": { "slideshow": { "slide_type": "slide" }, "tags": [] }, "outputs": [], "source": [ "# Find collocations in the texts\n", "finder = BigramCollocationFinder.from_words(sum(texts[:100], []))\n", "bgm = BigramAssocMeasures()\n", "score = bgm.mi_like\n", "collocations = {\"_\".join(bigram): pmi for bigram, pmi in finder.score_ngrams(score)}" ] }, { "cell_type": "code", "execution_count": 5, "id": "ecd44755", "metadata": { "slideshow": { "slide_type": "slide" }, "tags": [] }, "outputs": [ { "data": { "text/html": [ "
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bigramscore
0united_states7.111111
1et_al5.142857
2greatly_appreciated3.000000
3hymenaeus_beta3.000000
4proving_existence3.000000
53d_studio2.666667
6mozumder_proving2.250000
7_equinox_iii2.000000
8alex_delvecchio2.000000
9annals_cirp2.000000
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" ], "text/plain": [ " bigram score\n", "0 united_states 7.111111\n", "1 et_al 5.142857\n", "2 greatly_appreciated 3.000000\n", "3 hymenaeus_beta 3.000000\n", "4 proving_existence 3.000000\n", "5 3d_studio 2.666667\n", "6 mozumder_proving 2.250000\n", "7 _equinox_iii 2.000000\n", "8 alex_delvecchio 2.000000\n", "9 annals_cirp 2.000000" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Top 10 collocations\n", "collocations = pd.DataFrame(\n", " sorted(collocations.items(), key=lambda x: x[1], reverse=True),\n", " columns=[\"bigram\", \"score\"],\n", ")\n", "collocations.head(10)" ] }, { "attachments": {}, "cell_type": "markdown", "id": "1eeb6d38", "metadata": {}, "source": [ "## Computing Coherence Scores\n", "\n", "Next, we create some random topics and a dictionary with the vocabulary of the texts. We instantiate the CoherenceModel with the topics, texts, and dictionary, specifying the coherence measures. We compute the coherence scores for each topic and prepare the data for visualization.\n" ] }, { "cell_type": "code", "execution_count": 6, "id": "0f30ef52", "metadata": { "slideshow": { "slide_type": "slide" }, "tags": [] }, "outputs": [], "source": [ "# Create some random topics\n", "topics = [\n", " [\"car\", \"engine\", \"tire\", \"gasoline\", \"speed\"],\n", " [\"dog\", \"cat\", \"animal\", \"pet\", \"veterinarian\"],\n", " [\"movie\", \"director\", \"actor\", \"scene\", \"cinema\"],\n", " [\"music\", \"song\", \"artist\", \"instrument\", \"concert\"],\n", "]\n", "\n", "# Create a dictionary with the vocabulary\n", "word2id = Dictionary(texts)\n", "\n", "# Instantiate the CoherenceModel\n", "cm = CoherenceModel(topics=topics, texts=texts, coherence=\"c_v\", dictionary=word2id)\n", "\n", "# Compute coherence scores per topic\n", "coherence_per_topic = cm.get_coherence_per_topic()\n", "\n", "# Instantiate the CoherenceModel with c_uci coherence measure\n", "cm_uci = CoherenceModel(\n", " topics=topics, texts=texts, coherence=\"c_uci\", dictionary=word2id\n", ")\n", "\n", "# Compute coherence scores per topic\n", "coherence_per_topic_uci = cm_uci.get_coherence_per_topic()\n", "\n", "# Instantiate the CoherenceModel with u_mass coherence measure\n", "cm_umass = CoherenceModel(\n", " topics=topics, texts=texts, coherence=\"u_mass\", dictionary=word2id\n", ")\n", "\n", "# Compute coherence scores per topic\n", "coherence_per_topic_umass = cm_umass.get_coherence_per_topic()\n" ] }, { "cell_type": "code", "execution_count": 7, "id": "461635a1", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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TopicCoherenceMeasure
0car, engine, tire, gasoline, speed0.702137c_v
1dog, cat, animal, pet, veterinarian0.326970c_v
2movie, director, actor, scene, cinema0.238576c_v
3music, song, artist, instrument, concert0.333913c_v
4car, engine, tire, gasoline, speed-0.406149c_uci
5dog, cat, animal, pet, veterinarian-3.889160c_uci
6movie, director, actor, scene, cinema-7.223855c_uci
7music, song, artist, instrument, concert-6.404229c_uci
8car, engine, tire, gasoline, speed-2.893048u_mass
9dog, cat, animal, pet, veterinarian-6.618890u_mass
10movie, director, actor, scene, cinema-15.593594u_mass
11music, song, artist, instrument, concert-9.202692u_mass
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" ], "text/plain": [ " Topic Coherence Measure\n", "0 car, engine, tire, gasoline, speed 0.702137 c_v\n", "1 dog, cat, animal, pet, veterinarian 0.326970 c_v\n", "2 movie, director, actor, scene, cinema 0.238576 c_v\n", "3 music, song, artist, instrument, concert 0.333913 c_v\n", "4 car, engine, tire, gasoline, speed -0.406149 c_uci\n", "5 dog, cat, animal, pet, veterinarian -3.889160 c_uci\n", "6 movie, director, actor, scene, cinema -7.223855 c_uci\n", "7 music, song, artist, instrument, concert -6.404229 c_uci\n", "8 car, engine, tire, gasoline, speed -2.893048 u_mass\n", "9 dog, cat, animal, pet, veterinarian -6.618890 u_mass\n", "10 movie, director, actor, scene, cinema -15.593594 u_mass\n", "11 music, song, artist, instrument, concert -9.202692 u_mass" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import numpy as np\n", "\n", "# Combine all coherence scores into a single DataFrame\n", "all_coherence_scores = pd.DataFrame(\n", " {\n", " \"Topic\": [\", \".join(t) for t in topics] * 3,\n", " \"Coherence\": np.concatenate(\n", " [coherence_per_topic, coherence_per_topic_uci, coherence_per_topic_umass]\n", " ),\n", " \"Measure\": [\"c_v\"] * len(topics)\n", " + [\"c_uci\"] * len(topics)\n", " + [\"u_mass\"] * len(topics),\n", " }\n", ")\n", "all_coherence_scores\n" ] }, { "attachments": {}, "cell_type": "markdown", "id": "27440b36", "metadata": {}, "source": [ "Here, we have the coherence scores for four different topics using three coherence measures: $c_{v}$, $c_{uci}$, and $u_{mass}$. Let's interpret the results:\n", "\n", "1. Topic 1: \"car, engine, tire, gasoline, speed\"\n", " - $c_{v}$ coherence score: 0.702137\n", " - $c_{uci}$ coherence score: -0.406149\n", " - $u_{mass}$ coherence score: -2.893048\n", "\n", "Topic 1 seems to be the most coherent topic according to the $c_{v}$ measure, while it has a relatively low negative score for the $c_{uci}$ and $u_{mass}$ measures. This suggests that this topic is quite coherent and has a strong relationship between the words, especially when considering the $c_{v}$ measure.\n", "\n", "2. Topic 2: \"dog, cat, animal, pet, veterinarian\"\n", " - $c_{v}$ coherence score: 0.326970\n", " - $c_{uci}$ coherence score: -3.889160\n", " - $u_{mass}$ coherence score: -6.618890\n", "\n", "Topic 2 has a lower $c_{v}$ coherence score than Topic 1, but it's still positive, indicating a reasonable level of coherence. However, the $c_{uci}$ and $u_{mass}$ measures show relatively large negative scores, suggesting a weaker relationship between the words in this topic.\n", "\n", "3. Topic 3: \"movie, director, actor, scene, cinema\"\n", " - $c_{v}$ coherence score: 0.238576\n", " - $c_{uci}$ coherence score: -7.223855\n", " - $u_{mass}$ coherence score: -15.593594\n", "\n", "Topic 3 has the lowest $c_{v}$ coherence score among the four topics, but it's still positive. However, it has the largest negative scores for both the $c_{uci}$ and $u_{mass}$ measures, which suggests that the relationship between the words in this topic is the weakest among the four topics.\n", "\n", "4. Topic 4: \"music, song, artist, instrument, concert\"\n", " - $c_{v}$ coherence score: 0.333913\n", " - $c_{uci}$ coherence score: -6.404229\n", " - $u_{mass}$ coherence score: -9.202692\n", "\n", "Topic 4 has a similar $c_{v}$ coherence score as Topic 2, indicating a decent level of coherence. The $c_{uci}$ and $u_{mass}$ measures show large negative scores, but they are not as low as Topic 3's scores.\n", "\n", "In summary, according to the $c_{v}$ coherence measure, Topic 1 is the most coherent, followed by Topics 2, 4, and 3. However, the $c_{uci}$ and $u_{mass}$ measures give relatively large negative scores for all the topics, with Topic 3 consistently being the least coherent. It's important to note that the scale and interpretation of the coherence measures differ, so comparing the raw scores across measures can be misleading.\n" ] }, { "attachments": {}, "cell_type": "markdown", "id": "8b7518d4", "metadata": {}, "source": [ "## Visualizing the Coherence Scores\n", "\n", "Finally, we visualize the coherence scores.\n" ] }, { "cell_type": "code", "execution_count": 11, "id": "ebad1004", "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": { "image/png": { "height": 739, "width": 1236 } }, "output_type": "display_data" } ], "source": [ "# Set plot style\n", "plt.style.use(\"ggplot\")\n", "\n", "# Create a bar plot with subplots for each coherence measure\n", "fig, axes = plt.subplots(nrows=1, ncols=3, figsize=(15, 5))\n", "fig.suptitle(\"Coherence Scores per Topic and Measure\")\n", "\n", "for idx, measure in enumerate([\"c_v\", \"c_uci\", \"u_mass\"]):\n", " ax = axes[idx]\n", " data = all_coherence_scores[all_coherence_scores[\"Measure\"] == measure]\n", " data.plot(kind=\"bar\", x=\"Topic\", y=\"Coherence\", ax=ax, legend=False)\n", " ax.set_title(measure)\n", " ax.set_xlabel(\"\")\n", "\n", "# Add a y-label to the leftmost subplot\n", "axes[0].set_ylabel(\"Coherence Score\")\n", "\n", "plt.show()\n" ] }, { "attachments": {}, "cell_type": "markdown", "id": "19a594be", "metadata": {}, "source": [ "## Summary\n", "\n", "By calculating topic coherence scores, we can gain insights into the quality of the topics generated by a topic model. Higher coherence scores indicate that the words in the topic are more related to each other, forming a coherent concept, while lower coherence scores suggest that the words in the topic may not be related or form a clear concept.\n" ] }, { "attachments": {}, "cell_type": "markdown", "id": "e7633ddc", "metadata": {}, "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.12" }, "vscode": { "interpreter": { "hash": "d4d1e4263499bec80672ea0156c357c1ee493ec2b1c70f0acce89fc37c4a6abe" } } }, "nbformat": 4, "nbformat_minor": 5 }