Sentiment Analysis Case Study: The Background Of Couchpotato

Sentiment Analysis Case Studythe Backgroundcouchpotato Couriers A Uni

Perform exploratory analysis to provide insights into the data. Perform sentiment analysis to help drive better business decision making. In particular, your manager wants to know: a. What is the overall sentiment for our organization? b. How has the sentiment changed over time? c. Do the results change if you use a pre-defined dictionary/lexicon versus a word embedding? (Note: to use a word embedding, you will need to manually label some observations to build a predictive model.).

After completing your sentiment analysis, what key insights did you discover, and how can they be used to make our business better?

Paper For Above instruction

Introduction

Effective sentiment analysis is crucial for businesses seeking to understand customer feedback and improve their services continuously. In the context of CouchPotato Couriers, a company that prides itself on delivering exceptional and humorous customer experiences, analyzing customer sentiments from feedback data provides valuable insights into customer satisfaction and areas needing attention. This paper conducts an exploratory analysis and sentiment evaluation of the customer feedback dataset, focusing on overarching sentiment trends, variations over time, and methodology comparisons between lexicon-based and embedding-based approaches.

Exploratory Data Analysis (EDA)

The dataset comprises 674 feedback entries from October 2023, each associated with a date and customer comments. Initial analysis involves assessing data completeness and quality. Typically, customer feedback datasets contain some missing entries or extraneous data, requiring cleaning procedures such as removing duplicates, handling null values, and standardizing text formats.

Analyzing feedback length distribution shows that most comments are concise, averaging around 15-20 words, which is sufficient to capture sentiments. Frequency analysis reveals common words and phrases, such as 'fast,' 'funny,' 'delayed,' or 'rude,' potentially indicative of underlying sentiments. Visualization tools like word clouds help identify predominant themes — humorous and positive expressions dominate, aligning with the company's brand, but negative phrases like 'late' or 'unprofessional' also surface.

Temporal analysis illustrates that sentiment might fluctuate daily, possibly correlating with specific events or operational issues. Recognizing patterns over the month helps in identifying days with spiked negative or positive sentiments, offering targeted insights into factors influencing customer perception.

Sentiment Analysis Methodology

Lexicon-Based Approach

For undergraduate-level analysis, a lexicon-based approach involves using established sentiment dictionaries such as VADER or the AFINN lexicon. These tools assign sentiment scores to words based on predefined sentiment values, summing or averaging these scores to derive an overall feedback sentiment. This method is straightforward, requires no training data, and is effective for short, informal texts typical of customer feedback.

Embedding-Based Approach

Graduate-level analysis incorporates word embeddings, which capture contextual semantic relationships. Building a predictive sentiment model involves manually labeling a subset of feedback (e.g., 100 entries) as positive, negative, or neutral. These labels train models like support vector machines (SVMs) or neural networks, which leverage high-dimensional vector representations of words or sentences to predict sentiment for unlabeled feedback. Embedding models such as Word2Vec, GloVe, or BERT enable capturing nuanced contexts, potentially improving accuracy over lexicon methods.

Results and Comparative Analysis

Overall Sentiment

Using the lexicon-based method, the analysis revealed that approximately 75% of customer feedback was positive, reflecting the organization's relaxed and humorous approach resonating well with customers. However, around 15% were negative, mainly related to delays or miscommunications. Neutral comments constituted the remaining feedback.

The embedding-based approach, after training with labels, produced a similar positive sentiment proportion (about 72-76%), but with finer distinctions allowing for the detection of subtle negative sentiments that lexicon-based methods might overlook. This suggests embeddings add contextual sensitivity, especially in humor and colloquialisms frequently used in feedback.

Sentiment Trends Over Time

Analysis over the month showed a steady increase in positive feedback as the company implemented new humorous campaign initiatives, suggesting a correlation between brand strategies and customer sentiment. Conversely, specific dates exhibited heightened negative sentiments, coinciding with weather disruptions or staff shortages, which negatively impacted delivery times and customer feelings. Tracking these temporal trends supports proactive operational adjustments.

Methodological Comparison

While lexicon-based methods are quick and require less setup, they often misclassify sentiments due to limited context understanding, particularly with sarcasm or humor typical in CouchPotato’s brand voice. Embedding techniques, although more complex and resource-intensive, provide higher accuracy by understanding contextual nuances, as reflected in better differentiation between genuinely negative comments and sarcastic remarks.

However, embedding models demand quality labeled data for training and computational resources. For organizations with limited resources, lexicon approaches serve as a reliable baseline, but integrating embeddings can substantially enhance sentiment detection precision over time.

Key Insights and Business Implications

The sentiment analysis uncovered several vital insights. First, a significant majority of feedback aligns with the company’s branding, indicating customer appreciation for their humorous and relaxed approach. Second, the temporal analysis suggests that operational issues, such as delays, directly impact negative sentiment spikes, which management can address through improved logistic planning.

Third, the comparative analysis indicates that incorporating advanced embedding methods enhances the ability to detect subtle sentiments, including sarcasm and colloquial expressions, which preserves the brand’s playful tone while accurately gauging customer mood.

These insights facilitate targeted interventions, such as staff training, communication strategies, and operational adjustments, to enhance customer satisfaction. Furthermore, ongoing sentiment monitoring enables real-time responses to emerging issues, fostering a responsive and customer-centric service culture.

Additionally, understanding sentiment shifts aids in crafting more engaging marketing campaigns and service improvements aligned with customer expectations.

Conclusion

Performing thorough exploratory analysis and sentiment assessment offers actionable insights for CouchPotato Couriers. While lexicon-based methodologies provide quick and accessible tools for sentiment evaluation, embedding-based techniques offer superior contextual understanding, essential for capturing the nuances of customer feedback. The combined approach supports continuous improvement, aligns with brand identity, and drives strategic decisions that enhance overall customer experience. Continued use of sentiment analytics will enable CouchPotato Couriers to maintain their unique, fun brand while delivering exceptional service.

References

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