May 22, 2025, 7:04 PM
chapter_study_3.xlsx
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chapter_study_3.xlsx has been successfully loaded. It contains several columns, including Start Date, End Date, Progress, Duration (in seconds), and many more. The first few rows of the dataset have been displayed above for your reference.nrclex package, which enables emotion analysis using the NRC Emotion Lexicon, has been successfully installed. You can now use this package to perform emotion analysis on text data.stood_out. Create a new variable called "stood_out_emotions" that contains a list of emotion words that appear in each row of stood_out.stood_out_emotions variable has been successfully created. It contains a list of words from the stood_out column for each row, which can be used for further emotion analysis.What stood out to me the most about the clip was...[What, stood, out, to, me, the, most, about, the, clip, was, ...]I was more interested in the Inuet culture and...[I, was, more, interested, in, the, Inuet, culture, and, ...]-99[99]stood_out. I want the stood_out_emotions variable to contain only words that nrclex says are associated with emotions. For example, stood_out_emotions should exclude all words that are pronouns or that are intensity modifiers such as "really" or "very." The only words that should appear in stood_out_emotions are emotion words.NRCLex library. The error occurred because the word being checked was not found in the affect_dict. This might be due to the fact that the affect_dict does not contain all words, only those associated with emotions.affect_dict. Let's modify the approach to handle this scenario.stood_out_emotions variable has been successfully updated to contain only words associated with emotions from the stood_out column. Here are the first few entries:What stood out to me the most about the clip was...[passion, appreciation, undying, personal, moral]I was more interested in the Inuet culture and...[interested, culture, inspire, forced]-99[]stood_out column. Each bar represents an emotion word and its corresponding frequency in the dataset.stood_out column. If you have any further questions or need more detailed analysis, feel free to ask!unique_emotion_words.csv
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stood_out to determine which of these words appear in each response. Store the unique list of emotion words in a new column called stood_out_emotions_2stood_out_emotions_2 has been successfully created, containing the unique list of emotion words from the simplified list that appear in each response of the stood_out column. The first few rows of this new column are displayed above.observer_emotion_words.csv
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observer_emotion_words.csv file has been completed:emotion_words_2.csv
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emotion_words_2.csv has been loaded successfully without any syntax errors.chapter_study_3.xlsx
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stood_out in the file chapter_study_3.xlsx, check if any of the words in "similar words" appear in the text. Store all matching words in a new variable called "stood_out_emotion_matches," and then store a list of matching category labels from the "word" column in another new variable called "stood_out_emotion_categories."emotion_words_2.csv has been loaded successfully. It contains two main columns: Word and Similar Words. The Similar Words column contains lists of words associated with each categorical label in the Word column.stood_out column in the chapter_study_3.xlsx file to find any matching words from the Similar Words column and store the results in new variables.stood_out column contains non-string values, which led to a TypeError when trying to iterate over them.stood_out column is treated as a string before performing any operations. We can convert the column to a string type and then proceed with the matching process.stood_out column of the chapter_study_3.xlsx file, the words from the Similar Words column in emotion_words_2.csv that appear in the text have been identified and stored in two new variables:stood_out_emotion_matches: Contains the list of matching words found in the text.stood_out_emotion_categories: Contains the list of category labels corresponding to the matching words.stood_out text along with the matching words and their categories.stood_out text from the chapter_study_3.xlsx file.chapterchapter have been identified and are displayed in the table above. Each row represents a chapter along with its most frequently occurring emotion category and the count of occurrences.chapterchapter has been created. Each row represents a chapter, and each column represents an emotion category. The values indicate the count of occurrences of each emotion category within each chapter.chapterchapterchapterchapter. The share column represents the proportion of each emotion category's occurrences within a specific chapter relative to its total occurrences across all chapters.chapter , by chapterstood_outstood_out column are:stood_out text, indicating the prevalent emotions expressed in the dataset.stood_out responses that matched on the contented categoryemotion_words_3.csv
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emotion_words_3.csv file. For each row in the stood_out column of the chapter_study_3.xlsx file, matching words from the Similar Words column have been identified and stored in the stood_out_emotion_matches variable. Corresponding category labels from the Word column have been stored in the stood_out_emotion_categories variable.stood_out text along with the matching words and their categories.emotion_words_3.csv file.emotion_words_3_updated.csv
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chapter_study_3.xlsx has been processed to identify matching words from the emotion_words_3_updated.csv file. The stood_out_emotion_matches and stood_out_emotion_categories columns have been updated with the relevant matches and categories.