"Sentimentr" Example Sentences
1. The sentimentr package helped me analyze the emotions in my text data.
2. I used the sentimentr function in R to classify the sentiment of my Twitter data.
3. The sentimentr algorithm accurately identified the positive sentiment in the customer reviews.
4. With sentimentr, I can quickly determine the overall sentiment of a large corpus of documents.
5. The sentimentr library provides a simple and intuitive way to analyze sentiment in text.
6. I was impressed by the sentimentr analysis of the political speeches, which revealed hidden biases.
7. The sentimentr plugin for Excel allows me to quickly analyze sentiment in customer feedback surveys.
8. The sentimentr tool helped me classify the sentiment of customer reviews into positive, negative, and neutral categories.
9. Using sentimentr, I was able to identify the key factors affecting customer sentiment towards our brand.
10. Sentimentr has become a vital tool in my market research toolkit, helping me understand customer sentiment towards new products.
11. The sentimentr package is highly customizable and can be configured to analyze sentiment in multiple languages.
12. The sentimentr analysis showed a significant shift in public sentiment towards the company, following a major PR crisis.
13. Using sentimentr, I was able to identify the specific words that evoked positive or negative sentiment in my text data.
14. The sentimentr algorithm correctly identified the sarcasm in some of the social media posts, which had otherwise been mistaken for positive sentiment.
15. Sentimentr allowed me to track the changes in public sentiment towards a political candidate over time.
16. The sentimentr output provided a convenient way to visualize the presence of positive or negative sentiment in the text data.
17. Using sentimentr, I was able to analyze the sentiment of the comments on my blog and identify trends over time.
18. I used sentimentr to analyze the sentiment of social media posts around a particular topic, which helped me understand the public perception towards it.
19. The sentimentr analysis revealed that the customer sentiment towards the company was heavily influenced by the pricing policies.
20. Sentimentr helped me identify the hidden emotional triggers in the text data that were otherwise difficult to detect.
21. Using sentimentr, I was able to accurately classify the sentiment of the reviews into positive, negative, or neutral categories.
22. I find sentimentr to be a highly useful tool in customer experience management, as it provides real-time insights into customer sentiment.
23. The sentimentr analysis revealed that the public sentiment towards the government had plummeted due to the recent policy changes.
24. With sentimentr, I was able to uncover the nuanced emotions in the text data that were not immediately obvious.
25. I used sentimentr to track the sentiment of product reviews over time, which helped me identify areas for improvement.
26. The sentimentr analysis of the customer feedback revealed that pricing was a major factor affecting customer satisfaction.
27. The sentimentr algorithm helped me classify the sentiment of the social media posts, which helped me understand the public perception towards my brand.
28. With sentimentr, I was able to quickly analyze the sentiment of the customer service transcripts and identify areas for improvement.
29. The sentimentr analysis showed that the company had a strong emotional connection with its customers, which was driving positive sentiment.
30. Using sentimentr, I was able to analyze the sentiment of the headlines in the news articles, which revealed the underlying emotions around a particular topic.
Common Phases
1. I love this movie; it's so funny and heartwarming.
2. This restaurant has terrible service; I would not recommend it.
3. The sunset over the ocean is breathtaking; it never gets old.
4. I'm frustrated with this situation; nothing seems to be going right.
5. The concert was incredible; the energy in the crowd was electric.
6. This book is captivating; I couldn't put it down.
7. I'm so disappointed in the outcome; I thought we had a chance.
8. This coffee is delicious; it's the perfect blend of flavors.
9. I'm feeling anxious about this meeting; I hope it goes well.
10. This workout is intense; I can feel my muscles burning.