Many of us watch TV shows for leisure within our daily mundane routines via various online streaming platforms (such as Amazon Prime or Netflix), each having its share of show categories. Whenever a new show is launched, its other details (such as episode plot summary) become available online.<\/p>\n\n\n\n
Each episode present in the show contains a story with some difference in their nature. To gain an understanding of them in the form of topics\/keywords, we will carry out the Topic Modeling concept of Natural Language Processing (NLP) in this article.<\/p>\n\n\n\n
The purpose here is to build a machine learning model that analyzes the data for each TV episode and summarizes them into shorter segments while identifying their underlying sentiments.<\/p>\n\n\n\n
Data<\/strong><\/h1>\n\n\n\nThe data used here comes from the show \u201cAlice in Borderland\u201d currently streaming on Netflix, which includes the following details \u2014<\/p>\n\n\n\n
\u00b7 Name of the Series<\/p>\n\n\n\n
\u00b7 Season No<\/p>\n\n\n\n
\u00b7 Episode Name\/No<\/p>\n\n\n\n
\u00b7 Episode Plot Summary<\/p>\n\n\n\n
The reference from the above pointers can be taken from the below link \u2014<\/p>\n\n\n\n\nhttps:\/\/en.wikipedia.org\/wiki\/Alice_in_Borderland_(TV_series)\n<\/div><\/figure>\n\n\n\nText Preparation<\/strong><\/h1>\n\n\n\nBefore building the algorithm, it is necessary to prepare the data for processing to perform better analysis to gain valuable insights. Some of the steps that are being followed in this section are as follows:<\/p>\n\n\n\n
a)<\/strong> Punctuation Removal \u2014<\/strong><\/p>\n\n\n\n\u00b7 All the punctuations such as \u2018!\u201d#$%&\u2019()*+,-.\/:;?@[\\]^_{|}~\u2019 contained in the text are removed.<\/p>\n\n\n\n
\u00b7 These are defined within the string library of Python.<\/p>\n\n\n\n
def<\/span> rem_punct<\/span>(txt<\/span>):\n pfree = \"\"<\/span>.join([i for<\/span> i in<\/span> txt if<\/span> i not<\/span> in<\/span> string.punctuation])\n return<\/span> pfree\nread[\"Episode Plot Summary\"<\/span>] = read[\"Episode Plot Summary\"<\/span>].apply(lambda<\/span> x:rem_punct(x))p<\/span><\/pre>\n\n\n\nb)<\/strong> Lowercasing Text \u2014<\/strong><\/p>\n\n\n\n\u00b7 One of the most common tasks in text processing is to convert the characters to lowercase to eliminate useless data or noise.<\/p>\n\n\n\n
read[\"Episode Plot Summary\"<\/span>] = read[\"Episode Plot Summary\"<\/span>].str<\/span>.lower()<\/span><\/pre>\n\n\n\nc)<\/strong> Tokenization \u2014<\/strong><\/p>\n\n\n\n\u00b7 Part of the NLP pipeline that converts the text into small tokens (words\/sentences).<\/p>\n\n\n\n
\u00b7 Here, word tokenization is performed where each word is subjected to deeper analysis to gather their importance in the text.<\/p>\n\n\n\n
read[\"Episode Plot Summary\"<\/span>] = read[\"Episode Plot Summary\"<\/span>].str<\/span>.split()<\/span><\/pre>\n\n\n\nd)<\/strong> Stopword Removal \u2014<\/strong><\/p>\n\n\n\n\u00b7 Stopwords are commonly used words that are removed since they add no value to the analysis.<\/p>\n\n\n\n
\u00b7 The NLTK library used here helps to remove the stopwords from the text.<\/p>\n\n\n\n
stp = nltk.corpus.stopwords.words('english'<\/span>)\ndef<\/span> rem_stopwords<\/span>(txt<\/span>):\n st_free = [i for<\/span> i in<\/span> txt if<\/span> i not<\/span> in<\/span> stp]\n return<\/span> st_free\nread[\"Episode Plot Summary\"<\/span>] = read[\"Episode Plot Summary\"<\/span>].apply(lambda<\/span> x:rem_stopwords(x))<\/span><\/pre>\n\n\n\ne)<\/strong> Lemmatization \u2014<\/strong><\/p>\n\n\n\n\u00b7 This is an algorithmic process followed to convert the word to its root form while also keeping its meaning intact.<\/p>\n\n\n\n
word_lemma = WordNetLemmatizer()\ndef<\/span> lemma<\/span>(txt<\/span>):\n l_txt = [word_lemma.lemmatize(w) for<\/span> w in<\/span> txt]\n return<\/span> l_txt\nread[\"Episode Plot Summary\"<\/span>] = read[\"Episode Plot Summary\"<\/span>].apply(lambda<\/span> x:lemma(x))\n\n<\/span><\/pre>\n\n\n\nModel Preparation<\/strong><\/h1>\n\n\n\nAfter preparing the data through the series of processing tasks in the previous section, we begin our demonstration for implementing the process for building the Topic Model.<\/p>\n\n\n\n
TF-IDF VECTORIZATION<\/strong><\/h2>\n\n\n\n\u00b7 TF-IDF (Term Frequency \u2014 Inverse Document Frequency) is based on the Bag of Words model that provides insights about the relevance of words in a document.<\/p>\n\n\n\n
\u00b7 Term Frequency (TF) measures the frequency of the word occurring in the document.<\/p>\n\n\n\n
\u00b7 Inverse Document Frequency (IDF) measures the importance of words.<\/p>\n\n\n\n
In this sub-process, the data is converted into a vectorized format to continue to the next step.<\/p>\n\n\n\n
LATENT DIRICHLET ALLOCATION (LDA) PROCESS<\/strong><\/h2>\n\n\n\nLDA is a model where \u2014<\/p>\n\n\n\n
\u00b7 Latent means that the model finds hidden topics in a document.<\/p>\n\n\n\n
\u00b7 Dirichlet indicates that the model assumes the distribution of topics in a document.<\/p>\n\n\n\n
The parameters used in the model when carrying out LDA on the vectorized data are as follows \u2014<\/p>\n\n\n\n
a) Number of Topics<\/p>\n\n\n\n
b) Learning method (the way by which the model assigns the topics to the documents)<\/p>\n\n\n\n
c) Number of iterations<\/p>\n\n\n\n
d) Random state<\/p>\n\n\n\n
#SEASON 1<\/span>\nread_season1 = read[read[\"Season No\"<\/span>]==\"Season 1\"<\/span>]\n\nfor<\/span> i in<\/span> range<\/span>(0<\/span>,len<\/span>(read_season1['Episode Plot Summary'<\/span>])):\n vect1 = TfidfVectorizer(stop_words = stp, max_features = 1000<\/span>)\n vect_text1 = vect1.fit_transform(read_season1['Episode Plot Summary'<\/span>][i])\n lda_model = LatentDirichletAllocation(n_components = 5<\/span>, learning_method = 'online'<\/span>, random_state = 42<\/span>, max_iter = 1<\/span>)\n lda_t = lda_model.fit_transform(vect_text1)\n\n vocab = vect1.get_feature_names()\n for<\/span> k, comp in<\/span> enumerate<\/span>(lda_model.components_):\n vocab_comp = zip<\/span>(vocab, comp)\n s_words = sorted<\/span>(vocab_comp, key = lambda<\/span> x:x[1<\/span>], reverse=True<\/span>)[:7<\/span>]\n print<\/span>(\"Topic \"<\/span>+str<\/span>(k)+\": \"<\/span>)\n for<\/span> t1 in<\/span> s_words:\n print<\/span>(t1[0<\/span>],end=\" \"<\/span>)\n print<\/span>(\"\\n\"<\/span>)\n\n\n#SEASON 2<\/span>\n read_season2 = read[read[\"Season No\"<\/span>]==\"Season 2\"<\/span>].reset_index()\n\nfor<\/span> i in<\/span> range<\/span>(0<\/span>,len<\/span>(read_season2['Episode Plot Summary'<\/span>])):\n vect1 = TfidfVectorizer(stop_words = stp, max_features = 1000<\/span>)\n vect_text1 = vect1.fit_transform(read_season2['Episode Plot Summary'<\/span>][i])\n\n\n lda_model = LatentDirichletAllocation(n_components = 5<\/span>, learning_method = 'online'<\/span>, random_state = 42<\/span>, max_iter = 1<\/span>)\n lda_t = lda_model.fit_transform(vect_text1)\n\n ## for j, topic in enumerate(lda_t[0]):<\/span>\n ## print(topic*100)<\/span>\n\n print<\/span>(read_season1[\"Episode No\"<\/span>][i].upper())\n\n vocab = vect1.get_feature_names()\n for<\/span> k, comp in<\/span> enumerate<\/span>(lda_model.components_):\n vocab_comp = zip<\/span>(vocab, comp)\n s_words = sorted<\/span>(vocab_comp, key = lambda<\/span> x:x[1<\/span>], reverse=True<\/span>)[:7<\/span>]\n for<\/span> t1 in<\/span> s_words:\n print<\/span>(t1[0<\/span>],end=\" \"<\/span>)\n print<\/span>(\"\\n\"<\/span>)\n\n<\/span><\/pre>\n\n\n\nWith the output generated by following the complete model preparation process in this section, we can obtain the main topics and their importance for each of the episodes in the TV series.<\/p>\n\n\n\n
Topic Sentiments<\/strong><\/h1>\n\n\n\nAfter obtaining the main keywords for the episodes in each season, we come over to analyze and understand the sentiment around them. The technique used here is VADER (Valence Aware Dictionary Sentiment Reasoner), a lexicon and rule-based tool designed for sentiments.<\/p>\n\n\n\n
The sentiment scores calculated here are based on the polarity returned as an output using this technique, which lies in the range of [-1, 1] where \u2014<\/p>\n\n\n\n
\u00b7 Positive sentiment lies between 0 and 1<\/p>\n\n\n\n
\u00b7 Negative sentiment lies between -1 and 0<\/p>\n\n\n\n
After determining the scores for episodes, we obtained the results in the form of average sentiment for each season, as shown in the below table \u2014<\/p>\n\n\n\n![\"\"\/](\"https:\/\/miro.medium.com\/v2\/resize:fit:313\/1*ZBcKgWRnbmH0zq_QcplZzg.png\")
Source: Author<\/figcaption><\/figure>\n\n\n\nBased on the given results, we observe that in both seasons, we are getting a negative sentiment. This signifies the show was built with a dark theme in mind, and it can fall into any of the following genre categories \u2014<\/p>\n\n\n\n
\u00b7 Violence<\/p>\n\n\n\n
\u00b7 Thriller<\/p>\n\n\n\n
\u00b7 Suspenseful<\/p>\n\n\n\n
\u00b7 Action, etc.<\/p>\n\n\n\n
Closing Notes<\/strong><\/h1>\n\n\n\nReaching the end of the article, we understood the practical implementation of topic modeling in the entertainment field and how the entire summary for the TV Show episodes can be defined into smaller segments.<\/p>\n\n\n\n
The NLP procedure carried out here processes the data to get it ready for further machine learning analysis and to identify the genre of the show based on the sentiment scores obtained for each season.<\/p>\n","protected":false},"excerpt":{"rendered":"
Introduction Many of us watch TV shows for leisure within our daily mundane routines via various online streaming platforms (such as Amazon Prime or Netflix), each having its share of show categories. Whenever a new show is launched, its other details (such as episode plot summary) become available online. Each episode present in the show […]<\/p>\n","protected":false},"author":110,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"customer_name":"","customer_description":"","customer_industry":"","customer_technologies":"","customer_logo":"","footnotes":""},"categories":[7],"tags":[],"coauthors":[208],"class_list":["post-8146","post","type-post","status-publish","format-standard","hentry","category-tutorials"],"yoast_head":"\n
NLP \u2014 Topic Modeling For a TV Series Episode Summary - Comet<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.comet.com\/site\/blog\/nlp-topic-modeling-for-a-tv-series-episode-summary\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"NLP \u2014 Topic Modeling For a TV Series Episode Summary\" \/>\n<meta property=\"og:description\" content=\"Introduction Many of us watch TV shows for leisure within our daily mundane routines via various online streaming platforms (such as Amazon Prime or Netflix), each having its share of show categories. Whenever a new show is launched, its other details (such as episode plot summary) become available online. Each episode present in the show […]\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.comet.com\/site\/blog\/nlp-topic-modeling-for-a-tv-series-episode-summary\" \/>\n<meta property=\"og:site_name\" content=\"Comet\" \/>\n<meta property=\"article:publisher\" content=\"https:\/\/www.facebook.com\/cometdotml\" \/>\n<meta property=\"article:published_time\" content=\"2023-11-09T16:32:00+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2025-04-24T17:04:31+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/miro.medium.com\/v2\/resize:fit:313\/1*ZBcKgWRnbmH0zq_QcplZzg.png\" \/>\n<meta name=\"author\" content=\"Kaustubh Verma\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:creator\" content=\"@Cometml\" \/>\n<meta name=\"twitter:site\" content=\"@Cometml\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"Kaustubh Verma\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"6 minutes\" \/>\n<!-- \/ Yoast SEO Premium plugin. -->","yoast_head_json":{"title":"NLP \u2014 Topic Modeling For a TV Series Episode Summary - Comet","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/www.comet.com\/site\/blog\/nlp-topic-modeling-for-a-tv-series-episode-summary","og_locale":"en_US","og_type":"article","og_title":"NLP \u2014 Topic Modeling For a TV Series Episode Summary","og_description":"Introduction Many of us watch TV shows for leisure within our daily mundane routines via various online streaming platforms (such as Amazon Prime or Netflix), each having its share of show categories. Whenever a new show is launched, its other details (such as episode plot summary) become available online. 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Text Preparation<\/strong><\/h1>\n\n\n\nBefore building the algorithm, it is necessary to prepare the data for processing to perform better analysis to gain valuable insights. Some of the steps that are being followed in this section are as follows:<\/p>\n\n\n\n
a)<\/strong> Punctuation Removal \u2014<\/strong><\/p>\n\n\n\n\u00b7 All the punctuations such as \u2018!\u201d#$%&\u2019()*+,-.\/:;?@[\\]^_{|}~\u2019 contained in the text are removed.<\/p>\n\n\n\n
\u00b7 These are defined within the string library of Python.<\/p>\n\n\n\n
def<\/span> rem_punct<\/span>(txt<\/span>):\n pfree = \"\"<\/span>.join([i for<\/span> i in<\/span> txt if<\/span> i not<\/span> in<\/span> string.punctuation])\n return<\/span> pfree\nread[\"Episode Plot Summary\"<\/span>] = read[\"Episode Plot Summary\"<\/span>].apply(lambda<\/span> x:rem_punct(x))p<\/span><\/pre>\n\n\n\nb)<\/strong> Lowercasing Text \u2014<\/strong><\/p>\n\n\n\n\u00b7 One of the most common tasks in text processing is to convert the characters to lowercase to eliminate useless data or noise.<\/p>\n\n\n\n
read[\"Episode Plot Summary\"<\/span>] = read[\"Episode Plot Summary\"<\/span>].str<\/span>.lower()<\/span><\/pre>\n\n\n\nc)<\/strong> Tokenization \u2014<\/strong><\/p>\n\n\n\n\u00b7 Part of the NLP pipeline that converts the text into small tokens (words\/sentences).<\/p>\n\n\n\n
\u00b7 Here, word tokenization is performed where each word is subjected to deeper analysis to gather their importance in the text.<\/p>\n\n\n\n
read[\"Episode Plot Summary\"<\/span>] = read[\"Episode Plot Summary\"<\/span>].str<\/span>.split()<\/span><\/pre>\n\n\n\nd)<\/strong> Stopword Removal \u2014<\/strong><\/p>\n\n\n\n\u00b7 Stopwords are commonly used words that are removed since they add no value to the analysis.<\/p>\n\n\n\n
\u00b7 The NLTK library used here helps to remove the stopwords from the text.<\/p>\n\n\n\n
stp = nltk.corpus.stopwords.words('english'<\/span>)\ndef<\/span> rem_stopwords<\/span>(txt<\/span>):\n st_free = [i for<\/span> i in<\/span> txt if<\/span> i not<\/span> in<\/span> stp]\n return<\/span> st_free\nread[\"Episode Plot Summary\"<\/span>] = read[\"Episode Plot Summary\"<\/span>].apply(lambda<\/span> x:rem_stopwords(x))<\/span><\/pre>\n\n\n\ne)<\/strong> Lemmatization \u2014<\/strong><\/p>\n\n\n\n\u00b7 This is an algorithmic process followed to convert the word to its root form while also keeping its meaning intact.<\/p>\n\n\n\n
word_lemma = WordNetLemmatizer()\ndef<\/span> lemma<\/span>(txt<\/span>):\n l_txt = [word_lemma.lemmatize(w) for<\/span> w in<\/span> txt]\n return<\/span> l_txt\nread[\"Episode Plot Summary\"<\/span>] = read[\"Episode Plot Summary\"<\/span>].apply(lambda<\/span> x:lemma(x))\n\n<\/span><\/pre>\n\n\n\nModel Preparation<\/strong><\/h1>\n\n\n\nAfter preparing the data through the series of processing tasks in the previous section, we begin our demonstration for implementing the process for building the Topic Model.<\/p>\n\n\n\n
TF-IDF VECTORIZATION<\/strong><\/h2>\n\n\n\n\u00b7 TF-IDF (Term Frequency \u2014 Inverse Document Frequency) is based on the Bag of Words model that provides insights about the relevance of words in a document.<\/p>\n\n\n\n
\u00b7 Term Frequency (TF) measures the frequency of the word occurring in the document.<\/p>\n\n\n\n
\u00b7 Inverse Document Frequency (IDF) measures the importance of words.<\/p>\n\n\n\n
In this sub-process, the data is converted into a vectorized format to continue to the next step.<\/p>\n\n\n\n
LATENT DIRICHLET ALLOCATION (LDA) PROCESS<\/strong><\/h2>\n\n\n\nLDA is a model where \u2014<\/p>\n\n\n\n
\u00b7 Latent means that the model finds hidden topics in a document.<\/p>\n\n\n\n
\u00b7 Dirichlet indicates that the model assumes the distribution of topics in a document.<\/p>\n\n\n\n
The parameters used in the model when carrying out LDA on the vectorized data are as follows \u2014<\/p>\n\n\n\n
a) Number of Topics<\/p>\n\n\n\n
b) Learning method (the way by which the model assigns the topics to the documents)<\/p>\n\n\n\n
c) Number of iterations<\/p>\n\n\n\n
d) Random state<\/p>\n\n\n\n
#SEASON 1<\/span>\nread_season1 = read[read[\"Season No\"<\/span>]==\"Season 1\"<\/span>]\n\nfor<\/span> i in<\/span> range<\/span>(0<\/span>,len<\/span>(read_season1['Episode Plot Summary'<\/span>])):\n vect1 = TfidfVectorizer(stop_words = stp, max_features = 1000<\/span>)\n vect_text1 = vect1.fit_transform(read_season1['Episode Plot Summary'<\/span>][i])\n lda_model = LatentDirichletAllocation(n_components = 5<\/span>, learning_method = 'online'<\/span>, random_state = 42<\/span>, max_iter = 1<\/span>)\n lda_t = lda_model.fit_transform(vect_text1)\n\n vocab = vect1.get_feature_names()\n for<\/span> k, comp in<\/span> enumerate<\/span>(lda_model.components_):\n vocab_comp = zip<\/span>(vocab, comp)\n s_words = sorted<\/span>(vocab_comp, key = lambda<\/span> x:x[1<\/span>], reverse=True<\/span>)[:7<\/span>]\n print<\/span>(\"Topic \"<\/span>+str<\/span>(k)+\": \"<\/span>)\n for<\/span> t1 in<\/span> s_words:\n print<\/span>(t1[0<\/span>],end=\" \"<\/span>)\n print<\/span>(\"\\n\"<\/span>)\n\n\n#SEASON 2<\/span>\n read_season2 = read[read[\"Season No\"<\/span>]==\"Season 2\"<\/span>].reset_index()\n\nfor<\/span> i in<\/span> range<\/span>(0<\/span>,len<\/span>(read_season2['Episode Plot Summary'<\/span>])):\n vect1 = TfidfVectorizer(stop_words = stp, max_features = 1000<\/span>)\n vect_text1 = vect1.fit_transform(read_season2['Episode Plot Summary'<\/span>][i])\n\n\n lda_model = LatentDirichletAllocation(n_components = 5<\/span>, learning_method = 'online'<\/span>, random_state = 42<\/span>, max_iter = 1<\/span>)\n lda_t = lda_model.fit_transform(vect_text1)\n\n ## for j, topic in enumerate(lda_t[0]):<\/span>\n ## print(topic*100)<\/span>\n\n print<\/span>(read_season1[\"Episode No\"<\/span>][i].upper())\n\n vocab = vect1.get_feature_names()\n for<\/span> k, comp in<\/span> enumerate<\/span>(lda_model.components_):\n vocab_comp = zip<\/span>(vocab, comp)\n s_words = sorted<\/span>(vocab_comp, key = lambda<\/span> x:x[1<\/span>], reverse=True<\/span>)[:7<\/span>]\n for<\/span> t1 in<\/span> s_words:\n print<\/span>(t1[0<\/span>],end=\" \"<\/span>)\n print<\/span>(\"\\n\"<\/span>)\n\n<\/span><\/pre>\n\n\n\nWith the output generated by following the complete model preparation process in this section, we can obtain the main topics and their importance for each of the episodes in the TV series.<\/p>\n\n\n\n
Topic Sentiments<\/strong><\/h1>\n\n\n\nAfter obtaining the main keywords for the episodes in each season, we come over to analyze and understand the sentiment around them. The technique used here is VADER (Valence Aware Dictionary Sentiment Reasoner), a lexicon and rule-based tool designed for sentiments.<\/p>\n\n\n\n
The sentiment scores calculated here are based on the polarity returned as an output using this technique, which lies in the range of [-1, 1] where \u2014<\/p>\n\n\n\n
\u00b7 Positive sentiment lies between 0 and 1<\/p>\n\n\n\n
\u00b7 Negative sentiment lies between -1 and 0<\/p>\n\n\n\n
After determining the scores for episodes, we obtained the results in the form of average sentiment for each season, as shown in the below table \u2014<\/p>\n\n\n\nSource: Author<\/figcaption><\/figure>\n\n\n\nBased on the given results, we observe that in both seasons, we are getting a negative sentiment. This signifies the show was built with a dark theme in mind, and it can fall into any of the following genre categories \u2014<\/p>\n\n\n\n
\u00b7 Violence<\/p>\n\n\n\n
\u00b7 Thriller<\/p>\n\n\n\n
\u00b7 Suspenseful<\/p>\n\n\n\n
\u00b7 Action, etc.<\/p>\n\n\n\n
Closing Notes<\/strong><\/h1>\n\n\n\nReaching the end of the article, we understood the practical implementation of topic modeling in the entertainment field and how the entire summary for the TV Show episodes can be defined into smaller segments.<\/p>\n\n\n\n
The NLP procedure carried out here processes the data to get it ready for further machine learning analysis and to identify the genre of the show based on the sentiment scores obtained for each season.<\/p>\n","protected":false},"excerpt":{"rendered":"
Introduction Many of us watch TV shows for leisure within our daily mundane routines via various online streaming platforms (such as Amazon Prime or Netflix), each having its share of show categories. Whenever a new show is launched, its other details (such as episode plot summary) become available online. Each episode present in the show […]<\/p>\n","protected":false},"author":110,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"customer_name":"","customer_description":"","customer_industry":"","customer_technologies":"","customer_logo":"","footnotes":""},"categories":[7],"tags":[],"coauthors":[208],"class_list":["post-8146","post","type-post","status-publish","format-standard","hentry","category-tutorials"],"yoast_head":"\n
NLP \u2014 Topic Modeling For a TV Series Episode Summary - Comet<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.comet.com\/site\/blog\/nlp-topic-modeling-for-a-tv-series-episode-summary\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"NLP \u2014 Topic Modeling For a TV Series Episode Summary\" \/>\n<meta property=\"og:description\" content=\"Introduction Many of us watch TV shows for leisure within our daily mundane routines via various online streaming platforms (such as Amazon Prime or Netflix), each having its share of show categories. Whenever a new show is launched, its other details (such as episode plot summary) become available online. Each episode present in the show […]\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.comet.com\/site\/blog\/nlp-topic-modeling-for-a-tv-series-episode-summary\" \/>\n<meta property=\"og:site_name\" content=\"Comet\" \/>\n<meta property=\"article:publisher\" content=\"https:\/\/www.facebook.com\/cometdotml\" \/>\n<meta property=\"article:published_time\" content=\"2023-11-09T16:32:00+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2025-04-24T17:04:31+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/miro.medium.com\/v2\/resize:fit:313\/1*ZBcKgWRnbmH0zq_QcplZzg.png\" \/>\n<meta name=\"author\" content=\"Kaustubh Verma\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:creator\" content=\"@Cometml\" \/>\n<meta name=\"twitter:site\" content=\"@Cometml\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"Kaustubh Verma\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"6 minutes\" \/>\n<!-- \/ Yoast SEO Premium plugin. -->","yoast_head_json":{"title":"NLP \u2014 Topic Modeling For a TV Series Episode Summary - Comet","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/www.comet.com\/site\/blog\/nlp-topic-modeling-for-a-tv-series-episode-summary","og_locale":"en_US","og_type":"article","og_title":"NLP \u2014 Topic Modeling For a TV Series Episode Summary","og_description":"Introduction Many of us watch TV shows for leisure within our daily mundane routines via various online streaming platforms (such as Amazon Prime or Netflix), each having its share of show categories. Whenever a new show is launched, its other details (such as episode plot summary) become available online. 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\u00b7 All the punctuations such as \u2018!\u201d#$%&\u2019()*+,-.\/:;?@[\\]^_{|}~\u2019 contained in the text are removed.<\/p>\n\n\n\n
\u00b7 These are defined within the string library of Python.<\/p>\n\n\n\n
def<\/span> rem_punct<\/span>(txt<\/span>):\n pfree = \"\"<\/span>.join([i for<\/span> i in<\/span> txt if<\/span> i not<\/span> in<\/span> string.punctuation])\n return<\/span> pfree\nread[\"Episode Plot Summary\"<\/span>] = read[\"Episode Plot Summary\"<\/span>].apply(lambda<\/span> x:rem_punct(x))p<\/span><\/pre>\n\n\n\nb)<\/strong> Lowercasing Text \u2014<\/strong><\/p>\n\n\n\n\u00b7 One of the most common tasks in text processing is to convert the characters to lowercase to eliminate useless data or noise.<\/p>\n\n\n\n
read[\"Episode Plot Summary\"<\/span>] = read[\"Episode Plot Summary\"<\/span>].str<\/span>.lower()<\/span><\/pre>\n\n\n\nc)<\/strong> Tokenization \u2014<\/strong><\/p>\n\n\n\n\u00b7 Part of the NLP pipeline that converts the text into small tokens (words\/sentences).<\/p>\n\n\n\n
\u00b7 Here, word tokenization is performed where each word is subjected to deeper analysis to gather their importance in the text.<\/p>\n\n\n\n
read[\"Episode Plot Summary\"<\/span>] = read[\"Episode Plot Summary\"<\/span>].str<\/span>.split()<\/span><\/pre>\n\n\n\nd)<\/strong> Stopword Removal \u2014<\/strong><\/p>\n\n\n\n\u00b7 Stopwords are commonly used words that are removed since they add no value to the analysis.<\/p>\n\n\n\n
\u00b7 The NLTK library used here helps to remove the stopwords from the text.<\/p>\n\n\n\n
stp = nltk.corpus.stopwords.words('english'<\/span>)\ndef<\/span> rem_stopwords<\/span>(txt<\/span>):\n st_free = [i for<\/span> i in<\/span> txt if<\/span> i not<\/span> in<\/span> stp]\n return<\/span> st_free\nread[\"Episode Plot Summary\"<\/span>] = read[\"Episode Plot Summary\"<\/span>].apply(lambda<\/span> x:rem_stopwords(x))<\/span><\/pre>\n\n\n\ne)<\/strong> Lemmatization \u2014<\/strong><\/p>\n\n\n\n\u00b7 This is an algorithmic process followed to convert the word to its root form while also keeping its meaning intact.<\/p>\n\n\n\n
word_lemma = WordNetLemmatizer()\ndef<\/span> lemma<\/span>(txt<\/span>):\n l_txt = [word_lemma.lemmatize(w) for<\/span> w in<\/span> txt]\n return<\/span> l_txt\nread[\"Episode Plot Summary\"<\/span>] = read[\"Episode Plot Summary\"<\/span>].apply(lambda<\/span> x:lemma(x))\n\n<\/span><\/pre>\n\n\n\nModel Preparation<\/strong><\/h1>\n\n\n\nAfter preparing the data through the series of processing tasks in the previous section, we begin our demonstration for implementing the process for building the Topic Model.<\/p>\n\n\n\n
TF-IDF VECTORIZATION<\/strong><\/h2>\n\n\n\n\u00b7 TF-IDF (Term Frequency \u2014 Inverse Document Frequency) is based on the Bag of Words model that provides insights about the relevance of words in a document.<\/p>\n\n\n\n
\u00b7 Term Frequency (TF) measures the frequency of the word occurring in the document.<\/p>\n\n\n\n
\u00b7 Inverse Document Frequency (IDF) measures the importance of words.<\/p>\n\n\n\n
In this sub-process, the data is converted into a vectorized format to continue to the next step.<\/p>\n\n\n\n
LATENT DIRICHLET ALLOCATION (LDA) PROCESS<\/strong><\/h2>\n\n\n\nLDA is a model where \u2014<\/p>\n\n\n\n
\u00b7 Latent means that the model finds hidden topics in a document.<\/p>\n\n\n\n
\u00b7 Dirichlet indicates that the model assumes the distribution of topics in a document.<\/p>\n\n\n\n
The parameters used in the model when carrying out LDA on the vectorized data are as follows \u2014<\/p>\n\n\n\n
a) Number of Topics<\/p>\n\n\n\n
b) Learning method (the way by which the model assigns the topics to the documents)<\/p>\n\n\n\n
c) Number of iterations<\/p>\n\n\n\n
d) Random state<\/p>\n\n\n\n
#SEASON 1<\/span>\nread_season1 = read[read[\"Season No\"<\/span>]==\"Season 1\"<\/span>]\n\nfor<\/span> i in<\/span> range<\/span>(0<\/span>,len<\/span>(read_season1['Episode Plot Summary'<\/span>])):\n vect1 = TfidfVectorizer(stop_words = stp, max_features = 1000<\/span>)\n vect_text1 = vect1.fit_transform(read_season1['Episode Plot Summary'<\/span>][i])\n lda_model = LatentDirichletAllocation(n_components = 5<\/span>, learning_method = 'online'<\/span>, random_state = 42<\/span>, max_iter = 1<\/span>)\n lda_t = lda_model.fit_transform(vect_text1)\n\n vocab = vect1.get_feature_names()\n for<\/span> k, comp in<\/span> enumerate<\/span>(lda_model.components_):\n vocab_comp = zip<\/span>(vocab, comp)\n s_words = sorted<\/span>(vocab_comp, key = lambda<\/span> x:x[1<\/span>], reverse=True<\/span>)[:7<\/span>]\n print<\/span>(\"Topic \"<\/span>+str<\/span>(k)+\": \"<\/span>)\n for<\/span> t1 in<\/span> s_words:\n print<\/span>(t1[0<\/span>],end=\" \"<\/span>)\n print<\/span>(\"\\n\"<\/span>)\n\n\n#SEASON 2<\/span>\n read_season2 = read[read[\"Season No\"<\/span>]==\"Season 2\"<\/span>].reset_index()\n\nfor<\/span> i in<\/span> range<\/span>(0<\/span>,len<\/span>(read_season2['Episode Plot Summary'<\/span>])):\n vect1 = TfidfVectorizer(stop_words = stp, max_features = 1000<\/span>)\n vect_text1 = vect1.fit_transform(read_season2['Episode Plot Summary'<\/span>][i])\n\n\n lda_model = LatentDirichletAllocation(n_components = 5<\/span>, learning_method = 'online'<\/span>, random_state = 42<\/span>, max_iter = 1<\/span>)\n lda_t = lda_model.fit_transform(vect_text1)\n\n ## for j, topic in enumerate(lda_t[0]):<\/span>\n ## print(topic*100)<\/span>\n\n print<\/span>(read_season1[\"Episode No\"<\/span>][i].upper())\n\n vocab = vect1.get_feature_names()\n for<\/span> k, comp in<\/span> enumerate<\/span>(lda_model.components_):\n vocab_comp = zip<\/span>(vocab, comp)\n s_words = sorted<\/span>(vocab_comp, key = lambda<\/span> x:x[1<\/span>], reverse=True<\/span>)[:7<\/span>]\n for<\/span> t1 in<\/span> s_words:\n print<\/span>(t1[0<\/span>],end=\" \"<\/span>)\n print<\/span>(\"\\n\"<\/span>)\n\n<\/span><\/pre>\n\n\n\nWith the output generated by following the complete model preparation process in this section, we can obtain the main topics and their importance for each of the episodes in the TV series.<\/p>\n\n\n\n
Topic Sentiments<\/strong><\/h1>\n\n\n\nAfter obtaining the main keywords for the episodes in each season, we come over to analyze and understand the sentiment around them. The technique used here is VADER (Valence Aware Dictionary Sentiment Reasoner), a lexicon and rule-based tool designed for sentiments.<\/p>\n\n\n\n
The sentiment scores calculated here are based on the polarity returned as an output using this technique, which lies in the range of [-1, 1] where \u2014<\/p>\n\n\n\n
\u00b7 Positive sentiment lies between 0 and 1<\/p>\n\n\n\n
\u00b7 Negative sentiment lies between -1 and 0<\/p>\n\n\n\n
After determining the scores for episodes, we obtained the results in the form of average sentiment for each season, as shown in the below table \u2014<\/p>\n\n\n\nSource: Author<\/figcaption><\/figure>\n\n\n\nBased on the given results, we observe that in both seasons, we are getting a negative sentiment. This signifies the show was built with a dark theme in mind, and it can fall into any of the following genre categories \u2014<\/p>\n\n\n\n
\u00b7 Violence<\/p>\n\n\n\n
\u00b7 Thriller<\/p>\n\n\n\n
\u00b7 Suspenseful<\/p>\n\n\n\n
\u00b7 Action, etc.<\/p>\n\n\n\n
Closing Notes<\/strong><\/h1>\n\n\n\nReaching the end of the article, we understood the practical implementation of topic modeling in the entertainment field and how the entire summary for the TV Show episodes can be defined into smaller segments.<\/p>\n\n\n\n
The NLP procedure carried out here processes the data to get it ready for further machine learning analysis and to identify the genre of the show based on the sentiment scores obtained for each season.<\/p>\n","protected":false},"excerpt":{"rendered":"
Introduction Many of us watch TV shows for leisure within our daily mundane routines via various online streaming platforms (such as Amazon Prime or Netflix), each having its share of show categories. Whenever a new show is launched, its other details (such as episode plot summary) become available online. Each episode present in the show […]<\/p>\n","protected":false},"author":110,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"customer_name":"","customer_description":"","customer_industry":"","customer_technologies":"","customer_logo":"","footnotes":""},"categories":[7],"tags":[],"coauthors":[208],"class_list":["post-8146","post","type-post","status-publish","format-standard","hentry","category-tutorials"],"yoast_head":"\n
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