{
"source_file": "0930",
"source_platform": "Medium",
"author": "Petr Koráb",
"title_listed": "Customer Satisfaction Measurement with N-gram and Sentiment Analysis",
"keywords": "",
"domain": "文本处理",
"content_type": "深度技术解析",
"is_paid_flag": "否",
"url": "https://towardsdatascience.com/customer-satisfaction-measurement-with-n-gram-and-sentiment-analysis-547e291c13a6/",
"publish_date": "2023-04-11",
"corpus_stage": "",
"notes": "",
"id": "b592afb823ab",
"collected_at": "2026-10-02T03:52:11",
"title": "Customer Satisfaction Measurement with N-gram and Sentiment Analysis | Towards Data Science",
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Learn more about the right text mining...\n[Petr Koráb](https://towardsdatascience.com/author/petrkorab/)\nApril 10, 20237 min read\n### Product reviews are an excellent source of information for qualified management decisions. Learn more about the right text mining techniques.\n\nPhoto by [Freepik](https://www.freepik.com/free-photo/collage-customer-experience-concept_25053683.htm#query=customer%20satisfaction%20data&position=5&from_view=search&track=ais) on Freepik\nLearn this step by step with the interactive [AI and Data Scientist](https://roadmap.sh/ai-data-scientist) roadmap.\n## Introduction\nHappy customers drive company growth. The five-word sentence explains everything about why we do our best to maximize customer satisfaction. Product reviews are one of the major data sources that large companies like [Amazon](https://www.amazon.com/gp/help/customer/display.html?nodeId=GL4WJF8BGV8VL6B8) and [Apple](https://www.apple.com/contact/feedback/#:~:text=To%20comment%20on%20a%20particular,and%20select%20the%20appropriate%20link.), middle-sized exporters including [Lentiamo](https://www.trustpilot.com/review/lentiamo.nl), and local companies running their Facebook pages collect. Reviews are typically collected repeatedly over time, and factors like quality shifts, marketing communications, and customer care friendliness impact the sentiment expressed by customers.\n\nNote: Image by author, based on the review of Karim (2011), Baker and Wurgler (2006), Merrin et al. (2013), and Eachempat et al. (2022)\nThe Business Intelligence (BI) role should be to analyze product reviews, identify potential problems, and develop hypotheses to solve them. In the next stage, these recommendations are scrutinized by other departments based on the company structure. _**This article will explain more closely the analytics of customer satisfaction measurement with product reviews data.**_\nThe-end-to end process includes:\n * How to make an exploratory analysis of time-series product reviews\n * How to quickly evaluate sentiment in product reviews over time\n * How to display the most frequent satisfaction factors over time\n\n\nWe'll work in Python, which most BI and data analysts routinely use.\n## 2. Data\nGetting unscraped product reviews data with a flexible data license is generally difficult. Synthetic data from [**Fake Reviews Dataset**](https://osf.io/tyue9/) distributed with [Attribution 4.0 International license](https://creativecommons.org/licenses/by/4.0/) is an excellent option in this case.\nThe data looks like this:\n\nImage 1. Fake Reviews Dataset\nThe _text_ column contains product reviews, and _period_ marks the review date. The subset we'll work with contains 3848 reviews of clothing, shoes, and jewelry products.\n## 3. Exploratory data analysis (EDA)\nEDA in this type of dataset should discover the **completeness** of the dataset for each period of interest and the **presence of noise** that does not bring valuable information – digits and special characters. We try to avoid the situation of highly imbalanced datasets containing very few reviews in some periods and noisy datasets with too many digits and special characters (/, @, &,;, ? etc.).\n**3.1. Checking for data completeness**\nFirst, let's check the data for completeness for each period. We make annual comparisons and therefore summarise the reviews for each year:\npython\n\n```\nimport pandas as pd\n\n# Calculate review frequencies by year\ndata['year'] = pd.DatetimeIndex(data['period']).year\nrows_count = data.groupby('year', as_index=False).year.value_counts()\nrows_count.columns=['year','reviews']\n\nimport matplotlib.pyplot as plt\n\n# Generate a line plot\nrows_count.plot.line(x='year',y = 'reviews')\nplt.show()\n```\n\nA simple line pot shows the yearly review frequencies. It is not necessary to make any graph formatting here:\n\nImage 2. Yearly product review frequencies. Image by author.\n* * *\n> The dataset is not largely imbalanced. We have about 200 and more reviews per year, which makes a solid dataset for sentient and n-gram analyses.\n**3.2. Calculating letter-to-other characters ratio**\nNext, let's check if the data does not consist mainly of numbers and special characters, which could bias later text mining procedures.\npython\n\n```\nimport re\n\n# Convert product reviews to a string\ntext = pd.Series.to_string(data['text'], index = False)\n\n# Remove newline characters\ntext = re.sub('n', '', text)\n\n# Calculate sum of numbers, letters, and spaces\nnumbers = sum(c.isdigit() for c in text)\nletters = sum(c.isalpha() for c in text)\nspaces = sum(c.isspace() for c in text)\nothers = len(text) - numbers - letters - spaces\n```\n\nWe calculate _cleanness_ and _dirtiness_ (the opposite) metrics that show the ratios of letters to other characters (numbers, spaces, and special characters):\npython\n\n```\n# Calculate metrics\ndirtiness = ((numbers + others) / len(text)) * 100\ncleanness = ((letters + spaces) / len(text)) * 100\n\nprint(dirtiness)\nprint(cleanness)\n```\n\nThe print output is:\n * dirtiness is 8.77 %\n * cleanness is 91.22 %\n\n\n> We have around 9 % of digits and special characters in the data. This volume of noise should not bias sentiment analysis in the later stage.\n## 4. Customer satisfaction measurement\nText mining attempts to find out: (1) how the customers' sentiment developed over time and (2) which factors contributed to changes in customer satisfaction. We'll use [Arabica](https://pypi.org/project/arabica/), a Python library for time-series text mining, to explore both.\n_**EDIT Jul 2023**_ _: Arabica has been updated. Check the_ _**documentation**_ _for the full list of parameters._\n### 4.1. Sentiment analysis\nArabica offers `coffee_break` module for sentiment and breakpoint analysis. Its [documentation](https://arabica.readthedocs.io/en/latest/Breakpoint%20identification.html) provides more details about the models and methodology of sentiment evaluation.\nThis code cleans data from punctuation and numbers, removes other redundant strings (_\"_ \n_\", \"/n\",_ and _\"Another Long String\"_ are used as examples), calculates sentiment for each review, aggregates the sentiment by year, and identifies two major break points in the sentiment time series:\npython\n\n```\nfrom arabica import coffee_break\n\ncoffee_break(text = data['text'],\n time = data['period'],\n date_format = 'us', # Use US-style date format to read dates\n preprocess = True, # Clean data - digits and punctuation\n skip = ['
', # Remove additional stop words\n '/n',\n 'Another Long String'],\n n_breaks = 2, # 2 breakpoints identified\n time_freq = 'Y') # Yearly aggregation\n```\n\nThe line plot with sentiment and breakpoints:\n\nImage 3. Sentiment analysis with breakpoints. Image by author.\nWe can see fluctuations in sentiment over time. These changes are not rapid as sentiment varies [0.6: 0.7]. The higher value of sentiment, the better customers perceive the products (and the opposite).\n> In 1991 and 2000, there were positive and negative breakpoints in sentiment. In the next stage, let's check what caused these shifts.\n### 4.2. Factors driving customer satisfaction\nWe'll use a heatmap of n-gram-frequencies from the `cappuccino` module to visually derive the factors that influenced customers' sentiment. Read the [documentation](https://arabica.readthedocs.io/en/latest/Heatmap.html) for more technical details.\nThis code plots a heatmap of the eight most frequent bigrams (i.e., two consecutive words) in each year. The data pre-processing includes cleaning from English stopwords, numbers, and three unwanted strings (_\"_ \n_\", \"/n\"_ , and _\"Another Long String\"_). Punctuation and blank rows are removed automatically:\npython\n\n```\nfrom arabica import cappuccino\n\ncappuccino(text = data['text'],\n time = data['period'],\n date_format = 'us', # Use US-style date format to parse dates\n plot = 'heatmap',\n ngram = 2, # N-gram size, 1 = unigram, 2 = bigram\n time_freq = 'Y', # Aggregation period, 'M' = monthly, 'Y' = yearly\n max_words = 8, # Display 8 most frequent bigrams for each period\n stopwords = ['english'], # Remove English stopwords\n skip = ['
', # Remove additional stop words\n '/n',\n 'Another Long String'],\n numbers = True, # Remove numbers\n lower_case = True) # Lowercase text\n```\n\nThe bigram heatmap:\n\nImage 4. Bigram heatmap. Image by author.\nBigram frequencies indicate that:\n * _**2000s drop**_ (missing _\"would recommend\", \"fits perfectly\", and \"fit perfect\"_ in the top 8), and high frequencies of _\"wide foot\"_ and _\"size fit\"_ suggest that we might be selling products to people that do not fit their size of foot. Note that we removed stop words such as _\"don't\"_.\n\n\n## Conclusions\nThis type of text-mining analytics should help make qualified **data-informed decisions** and improve the quality of our products and services. Based on the analytical results, we determine a set of hypotheses about what might be wrong and where is space for improvement.\nFrom the text mining analysis in this article, we can see potential problems with product quality. The products fit worse our customers' current needs. Receiving the reviews in 2001, the analysts should formulate a set of problem hypotheses such as:\n * _The new shoes don't fit well._\n * _We don't sell wide-foot shoes, but customers demand them._\n * _There's a quality problem in the production of new products._\n\n\nMany other potential problem hypotheses arise since you know your products better than anyone else. Other departments (Marketing, Customer Care, Logistics, ..) should find out where the problem is, then react with specific improvements, and fix the problem. The response, of course, depends on the company's size and structure and other firm-specific factors.\nThe jupyter notebook with the code and data is on my [GitHub](https://github.com/PetrKorab/Customer-Satisfaction-Measurement-with-N-gram-and-Sentiment-Analysis).\n_Did you like the article? You can invite me_[ _for coffee_](https://www.buymeacoffee.com/petrkorab) _and support my writing. You can also subscribe to my_[ _email list_](https://medium.com/subscribe/@petrkorab) _to get notified about my new articles. Thanks!_\n## References\nBaker, M., Wurgler, J., 2006. Investor sentiment and the cross-section of stock returns. _Journal of Finance_ 61 (4).\nEachempati, P., Srivastava, P. R., Kumar A., Munoz, J., Dursun D., 2022. [Can customer sentiment impact firm value?](https://www.sciencedirect.com/science/article/abs/pii/S0040162521006995?via%3Dihub) An integrated text mining approach. _Technological Forecasting & Social Change_ 174 (1).\nKarim, B., 2011. Corporate name change and shareholder wealth effect: empirical evidence in the French stock market. _Journal of Asset Management_ 12 (3).\nMerrin, R. P., Hoffmann, A. O., Pennings, J. M., 2013. 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Learn more about the right text mining...\n[Petr Koráb](https://towardsdatascience.com/author/petrkorab/)\nApril 10, 20237 min read\n### Product reviews are an excellent source of information for qualified management decisions. Learn more about the right text mining techniques.\n\nPhoto by [Freepik](https://www.freepik.com/free-photo/collage-customer-experience-concept_25053683.htm#query=customer%20satisfaction%20data&position=5&from_view=search&track=ais) on Freepik\nLearn this step by step with the interactive [AI and Data Scientist](https://roadmap.sh/ai-data-scientist) roadmap.\n## Introduction\nHappy customers drive company growth. The five-word sentence explains everything about why we do our best to maximize customer satisfaction. Product reviews are one of the major data sources that large companies like [Amazon](https://www.amazon.com/gp/help/customer/display.html?nodeId=GL4WJF8BGV8VL6B8) and [Apple](https://www.apple.com/contact/feedback/#:~:text=To%20comment%20on%20a%20particular,and%20select%20the%20appropriate%20link.), middle-sized exporters including [Lentiamo](https://www.trustpilot.com/review/lentiamo.nl), and local companies running their Facebook pages collect. Reviews are typically collected repeatedly over time, and factors like quality shifts, marketing communications, and customer care friendliness impact the sentiment expressed by customers.\n\nNote: Image by author, based on the review of Karim (2011), Baker and Wurgler (2006), Merrin et al. (2013), and Eachempat et al. (2022)\nThe Business Intelligence (BI) role should be to analyze product reviews, identify potential problems, and develop hypotheses to solve them. In the next stage, these recommendations are scrutinized by other departments based on the company structure. _**This article will explain more closely the analytics of customer satisfaction measurement with product reviews data.**_\nThe-end-to end process includes:\n * How to make an exploratory analysis of time-series product reviews\n * How to quickly evaluate sentiment in product reviews over time\n * How to display the most frequent satisfaction factors over time\n\n\nWe'll work in Python, which most BI and data analysts routinely use.\n## 2. Data\nGetting unscraped product reviews data with a flexible data license is generally difficult. Synthetic data from [**Fake Reviews Dataset**](https://osf.io/tyue9/) distributed with [Attribution 4.0 International license](https://creativecommons.org/licenses/by/4.0/) is an excellent option in this case.\nThe data looks like this:\n\nImage 1. Fake Reviews Dataset\nThe _text_ column contains product reviews, and _period_ marks the review date. The subset we'll work with contains 3848 reviews of clothing, shoes, and jewelry products.\n## 3. Exploratory data analysis (EDA)\nEDA in this type of dataset should discover the **completeness** of the dataset for each period of interest and the **presence of noise** that does not bring valuable information – digits and special characters. We try to avoid the situation of highly imbalanced datasets containing very few reviews in some periods and noisy datasets with too many digits and special characters (/, @, &,;, ? etc.).\n**3.1. Checking for data completeness**\nFirst, let's check the data for completeness for each period. We make annual comparisons and therefore summarise the reviews for each year:\npython\n\n```\nimport pandas as pd\n\n# Calculate review frequencies by year\ndata['year'] = pd.DatetimeIndex(data['period']).year\nrows_count = data.groupby('year', as_index=False).year.value_counts()\nrows_count.columns=['year','reviews']\n\nimport matplotlib.pyplot as plt\n\n# Generate a line plot\nrows_count.plot.line(x='year',y = 'reviews')\nplt.show()\n```\n\nA simple line pot shows the yearly review frequencies. It is not necessary to make any graph formatting here:\n\nImage 2. Yearly product review frequencies. Image by author.\n* * *\n> The dataset is not largely imbalanced. We have about 200 and more reviews per year, which makes a solid dataset for sentient and n-gram analyses.\n**3.2. Calculating letter-to-other characters ratio**\nNext, let's check if the data does not consist mainly of numbers and special characters, which could bias later text mining procedures.\npython\n\n```\nimport re\n\n# Convert product reviews to a string\ntext = pd.Series.to_string(data['text'], index = False)\n\n# Remove newline characters\ntext = re.sub('n', '', text)\n\n# Calculate sum of numbers, letters, and spaces\nnumbers = sum(c.isdigit() for c in text)\nletters = sum(c.isalpha() for c in text)\nspaces = sum(c.isspace() for c in text)\nothers = len(text) - numbers - letters - spaces\n```\n\nWe calculate _cleanness_ and _dirtiness_ (the opposite) metrics that show the ratios of letters to other characters (numbers, spaces, and special characters):\npython\n\n```\n# Calculate metrics\ndirtiness = ((numbers + others) / len(text)) * 100\ncleanness = ((letters + spaces) / len(text)) * 100\n\nprint(dirtiness)\nprint(cleanness)\n```\n\nThe print output is:\n * dirtiness is 8.77 %\n * cleanness is 91.22 %\n\n\n> We have around 9 % of digits and special characters in the data. This volume of noise should not bias sentiment analysis in the later stage.\n## 4. Customer satisfaction measurement\nText mining attempts to find out: (1) how the customers' sentiment developed over time and (2) which factors contributed to changes in customer satisfaction. We'll use [Arabica](https://pypi.org/project/arabica/), a Python library for time-series text mining, to explore both.\n_**EDIT Jul 2023**_ _: Arabica has been updated. Check the_ _**documentation**_ _for the full list of parameters._\n### 4.1. Sentiment analysis\nArabica offers `coffee_break` module for sentiment and breakpoint analysis. Its [documentation](https://arabica.readthedocs.io/en/latest/Breakpoint%20identification.html) provides more details about the models and methodology of sentiment evaluation.\nThis code cleans data from punctuation and numbers, removes other redundant strings (_\"_ \n_\", \"/n\",_ and _\"Another Long String\"_ are used as examples), calculates sentiment for each review, aggregates the sentiment by year, and identifies two major break points in the sentiment time series:\npython\n\n```\nfrom arabica import coffee_break\n\ncoffee_break(text = data['text'],\n time = data['period'],\n date_format = 'us', # Use US-style date format to read dates\n preprocess = True, # Clean data - digits and punctuation\n skip = ['
', # Remove additional stop words\n '/n',\n 'Another Long String'],\n n_breaks = 2, # 2 breakpoints identified\n time_freq = 'Y') # Yearly aggregation\n```\n\nThe line plot with sentiment and breakpoints:\n\nImage 3. Sentiment analysis with breakpoints. Image by author.\nWe can see fluctuations in sentiment over time. These changes are not rapid as sentiment varies [0.6: 0.7]. The higher value of sentiment, the better customers perceive the products (and the opposite).\n> In 1991 and 2000, there were positive and negative breakpoints in sentiment. In the next stage, let's check what caused these shifts.\n### 4.2. Factors driving customer satisfaction\nWe'll use a heatmap of n-gram-frequencies from the `cappuccino` module to visually derive the factors that influenced customers' sentiment. Read the [documentation](https://arabica.readthedocs.io/en/latest/Heatmap.html) for more technical details.\nThis code plots a heatmap of the eight most frequent bigrams (i.e., two consecutive words) in each year. The data pre-processing includes cleaning from English stopwords, numbers, and three unwanted strings (_\"_ \n_\", \"/n\"_ , and _\"Another Long String\"_). Punctuation and blank rows are removed automatically:\npython\n\n```\nfrom arabica import cappuccino\n\ncappuccino(text = data['text'],\n time = data['period'],\n date_format = 'us', # Use US-style date format to parse dates\n plot = 'heatmap',\n ngram = 2, # N-gram size, 1 = unigram, 2 = bigram\n time_freq = 'Y', # Aggregation period, 'M' = monthly, 'Y' = yearly\n max_words = 8, # Display 8 most frequent bigrams for each period\n stopwords = ['english'], # Remove English stopwords\n skip = ['
', # Remove additional stop words\n '/n',\n 'Another Long String'],\n numbers = True, # Remove numbers\n lower_case = True) # Lowercase text\n```\n\nThe bigram heatmap:\n\nImage 4. Bigram heatmap. Image by author.\nBigram frequencies indicate that:\n * _**2000s drop**_ (missing _\"would recommend\", \"fits perfectly\", and \"fit perfect\"_ in the top 8), and high frequencies of _\"wide foot\"_ and _\"size fit\"_ suggest that we might be selling products to people that do not fit their size of foot. Note that we removed stop words such as _\"don't\"_.\n\n\n## Conclusions\nThis type of text-mining analytics should help make qualified **data-informed decisions** and improve the quality of our products and services. Based on the analytical results, we determine a set of hypotheses about what might be wrong and where is space for improvement.\nFrom the text mining analysis in this article, we can see potential problems with product quality. The products fit worse our customers' current needs. Receiving the reviews in 2001, the analysts should formulate a set of problem hypotheses such as:\n * _The new shoes don't fit well._\n * _We don't sell wide-foot shoes, but customers demand them._\n * _There's a quality problem in the production of new products._\n\n\nMany other potential problem hypotheses arise since you know your products better than anyone else. Other departments (Marketing, Customer Care, Logistics, ..) should find out where the problem is, then react with specific improvements, and fix the problem. The response, of course, depends on the company's size and structure and other firm-specific factors.\nThe jupyter notebook with the code and data is on my [GitHub](https://github.com/PetrKorab/Customer-Satisfaction-Measurement-with-N-gram-and-Sentiment-Analysis).\n_Did you like the article? You can invite me_[ _for coffee_](https://www.buymeacoffee.com/petrkorab) _and support my writing. You can also subscribe to my_[ _email list_](https://medium.com/subscribe/@petrkorab) _to get notified about my new articles. Thanks!_\n## References\nBaker, M., Wurgler, J., 2006. Investor sentiment and the cross-section of stock returns. _Journal of Finance_ 61 (4).\nEachempati, P., Srivastava, P. R., Kumar A., Munoz, J., Dursun D., 2022. [Can customer sentiment impact firm value?](https://www.sciencedirect.com/science/article/abs/pii/S0040162521006995?via%3Dihub) An integrated text mining approach. _Technological Forecasting & Social Change_ 174 (1).\nKarim, B., 2011. Corporate name change and shareholder wealth effect: empirical evidence in the French stock market. _Journal of Asset Management_ 12 (3).\nMerrin, R. P., Hoffmann, A. O., Pennings, J. M., 2013. Customer satisfaction as a buffer against sentimental stock-price corrections. _Marketing Letters_ 24 (1).\n## Register Now to Continue Reading\n### Unlock Towards Data Science with a Free Account\nAll articles on Towards Data Science are available to registered users. 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