![Previous approach in Word-level
sentiment Extraction
• Previous works[Vo 2016, Li 2017] address this task by
automatically developing a word sentiment score
dictionary
• However, they cannot consider contexts
Input: In total, we are not in a bull market.
In total, we are not in a bull market.
Cannot consider contexts](https://image.slidesharecdn.com/decon2019-190713144711/85/Concept-Cloud-based-Sentiment-Visualization-for-Financial-Reviews-22-320.jpg)
![Our approach
• We solve this task by estimating
• P (•) : Original word-level sentiment
• R (•) : Contextual word-level sentiment
using the LRP method [L. Arras. et. al., 2017] with the RNN model
Document dataset: {Di}N
i=1 where Di = {wt
i }N
i=1
Sentiment tag:](https://image.slidesharecdn.com/decon2019-190713144711/85/Concept-Cloud-based-Sentiment-Visualization-for-Financial-Reviews-23-320.jpg)
![Our approach
• We solve this challenge by estimating
• P (•) : Original word-level sentiment
• R (•) : Contextual word-level sentiment
using the LRP method [L. Arras. et. al., 2017] with the RNN model
Document dataset: {Di}N
i=1 where Di = {wt
i }N
i=1
Sentiment tag:](https://image.slidesharecdn.com/decon2019-190713144711/85/Concept-Cloud-based-Sentiment-Visualization-for-Financial-Reviews-24-320.jpg)
![LRP-based Approach Process
• We estimate R (•) as follows
1. Develop a RNN model with LSTM cells using Text corpus
dataset including document and their positive or negative
sentiment tags
2. Estimate
• R (•) : Contextual word-level sentiment
using the LRP method [L. Arras. et. al., 2017] with the RNN model
In total, we are in a bull market.
R (•) : LRP + RNN (LSTM)
0.1 0.1 0.2 0.0 0.3 1.2 -0.1](https://image.slidesharecdn.com/decon2019-190713144711/85/Concept-Cloud-based-Sentiment-Visualization-for-Financial-Reviews-27-320.jpg)
![Comparison Method
• We compared our method with the following
comparison methods
• Word-level sentiment score assignment methods
• PMI
• FLW [D. T. Vo et. al., 2016]
• SONN [Q. Li et. al., 2017]](https://image.slidesharecdn.com/decon2019-190713144711/85/Concept-Cloud-based-Sentiment-Visualization-for-Financial-Reviews-38-320.jpg)
![Comparison Method
• We compared our method with the following
comparison methods
• Word-level sentiment score assignment methods
• PMI
• FLW [D. T. Vo et. al., 2016]
• SONN [Q. Li et. al., 2017]
• LR: Logistic Regression
• RNN with LSTM cells](https://image.slidesharecdn.com/decon2019-190713144711/85/Concept-Cloud-based-Sentiment-Visualization-for-Financial-Reviews-42-320.jpg)
![Previous approach in Word-level
sentiment Extraction
• Previous works[Vo 2016, Li 2017] address this task by
automatically developing a word sentiment score
dictionary
• However, they cannot consider contexts
Input: In total, we are not in a bull market.
In total, we are not in a bull market.
Cannot consider contexts](https://image.slidesharecdn.com/decon2019-190713144711/85/Concept-Cloud-based-Sentiment-Visualization-for-Financial-Reviews-56-320.jpg)
Concept Cloud-based Sentiment Visualization for Financial Reviews
- 1. Concept Cloud-based Sentiment Visualization for Financial Reviews Tomoki Ito*, Kota Tsubouchi**, Hiroki Sakaji*, Tatsuo Yamashita**, Kiyoshi Izumi* ** Graduate School of Engineering, The University of Tokyo * Yahoo Japan Corporation
- 2. Back ground • Online reviews are useful for decision making in the investment. • e.g., micro-blogs, SNS, and news articles…
- 3. Difficulty in Reading Large Volume of Reviews • To read all the posts should not be practical • the volume of the posts is sometimes very large.
- 4. Difficulty in Reading Large Volume of Reviews • To read all the posts should not be practical • the volume of the posts is sometimes very large. • Framework for visualizing the summary of the financial reviews is necessary
- 5. What is important for decision making in the investment ? • In the decision making process, the following two types of sentiments are important • Word-level sentiment score • Concept-level sentiment score
- 6. What is important for decision making in the investment ? • In the decision making process, the following two types of sentiments are important • Word-level sentiment score • Concept-level sentiment score
- 7. Word-level sentiment • Word-level sentiment means the sentiment scores in word units In total, we are in a bull market. 0.1 0.1 0.2 0.0 0.3 1.2 -0.1
- 8. Word-level sentiment • Word-level sentiment means the sentiment scores in word units • We should consider the context in this score assignment • e.g., sentiment shift by “not” In total, we are in a bull market. 0.1 0.1 0.2 0.0 0.3 1.2 -0.1 In total, we are not in a bull market. 0.1 0.1 0.2 0.0 0.3 -0.1 -0.1
- 9. What is important for decision making in the investment ? • In the decision making process, the following two types of sentiments are important • Word-level sentiment score • Concept-level sentiment score
- 10. Concept-level Sentiment • Concept-level sentiment means the sentiment scores in concept units • Concept means a set of similar words Up, Down, ↗︎↗︎ Delicious, Nasty, Palatable, … Clean, Dirty Trend: 0.5 Taste: -0.1 Cleanness: -0.2 Reviews
- 11. What is important for decision making in the investment ? • In the decision making process, the following two types of sentiments are important • Word-level sentiment score • Concept-level sentiment score
- 12. Purpose • This study aims to develop a method for visualizing • Word-level sentiment score, and • Concept-level sentiment score at the same time in a user-friendly way
- 13. Our Approach • We propose a novel text-visualization framework called CCSV Concept Cloud-based Sentiment Visualization
- 14. CCSV Example • Using CCSV, we can summarize reviews as follows (The price was inversed www) (The price was inversed.) (Go down below 3000yen. I cannot buy now.) ・ ・ ・ (Over 1000 reviews in five days)
- 15. CCSV Example • Using CCSV, we can summarize reviews as follows text-visualization results for a set of reviews for trading company X in September 25th, 2017 and September 30th, 2017 extracted from the Yahoo Financial Micro-blogs. • Color • Red: Positive Blue: Negative • Size: Volume of Sentiment
- 16. Contribution Our Contribution is summarized as follows • We propose a novel text-visualization framework called CCSV • We experimentally evaluated the validity of the CSCV using real dataset
- 17. Contribution Our Contribution is summarized as follows • We propose a novel text-visualization framework called CCSV • We experimentally evaluated the validity of the CCSV
- 18. Concept Cloud-based Sentiment Visualization • CCSV is constructed from the following three parts 1. Word-level sentiment Extraction 2. Concept-level sentiment Extraction 3. Word and Concept-level sentiment Visualization
- 19. Concept Cloud-based Sentiment Visualization • CCSV is constructed from the following three parts 1. Word-level sentiment Extraction 2. Concept-level sentiment Extraction 3. Word and Concept-level sentiment Visualization
- 20. Word-level sentiment Extraction • This step addresses the following contextual word-level sentiment score assignment task Input: In total, we are not in a bull market. In total, we are not in a bull market. ( Sentiment influence: polarity of “bull” is shifted by not) In total, we are not in a bull market. (Red and blue words has positive and negative sentiments, respectively ) Original Word-level Sentiment (sentiment score before considering contexts): Contextual Word-level Sentiment (sentiment score after considering contexts)
- 21. Task Setting • This step aims to assign word-level sentiment scores using only a text corpus dataset including reviews and their positive or negative sentiment tags In total, we are in a bull market. Review: Tag: Positive • We decided this task setting considering the practicality Text corpus dataset
- 22. Previous approach in Word-level sentiment Extraction • Previous works[Vo 2016, Li 2017] address this task by automatically developing a word sentiment score dictionary • However, they cannot consider contexts Input: In total, we are not in a bull market. In total, we are not in a bull market. Cannot consider contexts
- 23. Our approach • We solve this task by estimating • P (•) : Original word-level sentiment • R (•) : Contextual word-level sentiment using the LRP method [L. Arras. et. al., 2017] with the RNN model Document dataset: {Di}N i=1 where Di = {wt i }N i=1 Sentiment tag:
- 24. Our approach • We solve this challenge by estimating • P (•) : Original word-level sentiment • R (•) : Contextual word-level sentiment using the LRP method [L. Arras. et. al., 2017] with the RNN model Document dataset: {Di}N i=1 where Di = {wt i }N i=1 Sentiment tag:
- 25. LRP-based Approach Process • We estimate R (•) as follows
- 26. LRP-based Approach Process • We estimate R (•) as follows 1. Develop a RNN model with LSTM cells using Text corpus dataset including document and their positive or negative sentiment tags
- 27. LRP-based Approach Process • We estimate R (•) as follows 1. Develop a RNN model with LSTM cells using Text corpus dataset including document and their positive or negative sentiment tags 2. Estimate • R (•) : Contextual word-level sentiment using the LRP method [L. Arras. et. al., 2017] with the RNN model In total, we are in a bull market. R (•) : LRP + RNN (LSTM) 0.1 0.1 0.2 0.0 0.3 1.2 -0.1
- 28. Layer-wise Representation Propagation(LRP) • LRP is the method for interpreting Neural Networks • LRP calculates the relevance score of the input value to the output value • LRP can be used in the RNN with LSTM cells • the relevance score of each term from the LRP with the RNN is expected to consider contexts market is bull Positive Negative OutputInput (L. Arras. et. al., 2017) 28
- 29. Concept Cloud-based Sentiment Visualization • CCSV is constructed from the following three parts 1. Word-level sentiment Extraction • using the LRP method 2. Concept-level sentiment Extraction • with the word-level sentiments and K-means clustering method 3. Word and Concept-level sentiment Visualization • using Word Cloud method
- 30. Concept-level sentiment Extraction • This step calculates the concept-level sentiment score using the k-means clustering method Up: 0.5 Down: -0.2 ↗︎↗︎ : 0.6 delicious: 0.6 nasty: -0.7 Palatable: +1.5 Clean: +0.3 Dirty: -0.2 Reviews LRP Up: 0.5 Down: -0.2 ↗︎↗︎ : 0.6 delicious: 0.6 nasty: -0.7 Palatable: +1.5 Clean: +0.3 Dirty: -0.2 0.9 +1.4 +0.1 K-means method with word2vec
- 31. Concept Cloud-based Sentiment Visualization • CCSV is constructed from the following three parts 1. Word-level sentiment Extraction • using the LRP method 2. Concept-level sentiment Extraction • with the word-level sentiments and K-means clustering method 3. Word and Concept-level sentiment Visualization • using Word Cloud method
- 32. Word and Concept-level sentiment Visualization • This step visualize the word-level and concept-level sentiment scores using Tag Cloud Approach Up: 0.5 Down: -0.2 ↗︎↗︎ : 0.6 delicious: 0.6 nasty: -0.7 Palatable: +1.5 Clean: +0.3 Dirty: -0.2 0.9 +1.4 +0.1 Up Down ↗︎ ↗︎ Deliciousnasty Palatable Clean Dirty • Color • Red: Positive Blue: Negative • Size: Volume of Sentiment
- 33. Contribution Our Contribution is summarized as follows • We propose a novel text-visualization framework called CCSV • We experimentally evaluated the validity of the CCSV
- 34. Experimental Evaluation • We evaluated our method from two aspects using real textual datasets • Original Sentiment assignment property • Contextual sentiment assignment property
- 35. Dataset • We evaluated the validity of our approach using the following dataset • Text Corpus • Economic dataset: Current economy watchers survey • Train: 20,000 positive posts and 20,000 negative posts • Valid: 2,000 positive posts and 2,000 negative posts • Test: 4,000 positive posts and 4,000 negative posts • Yahoo dataset: Yahoo Finance micro-blogs between September • Train: 30,612 positive posts and 9,388 negative posts • Valid: 3,387 positive posts and 1,613 negative posts • Test: 7,538 positive posts and 2,462 negative posts
- 36. Experimental Evaluation • We evaluated our method from three aspects using real textual datasets • Original Sentiment assignment property • Contextual sentiment assignment property
- 37. Original Sentiment assignment property • How accurately P (•) presents the positive or negative polarity of each term in the word polarity list • Economic word polarity list • 348 positive and 391 negative words • We used this list when we estimated P (•) using the Economic dataset • Yahoo word polarity list • 422 positive and 372 negative words • We used this list when we estimated P (•) using the Yahoo dataset Good: Positive Bad: Negative Great: Positive Bullish: Positive ・ ・ ・ Word polarity list
- 38. Comparison Method • We compared our method with the following comparison methods • Word-level sentiment score assignment methods • PMI • FLW [D. T. Vo et. al., 2016] • SONN [Q. Li et. al., 2017]
- 39. Result 0.5 0.55 0.6 0.65 0.7 0.75 0.8 0.85 PMI LFW SONN LRP (Our Approach) Economic Dataset Yahoo Dataset Our method outperformed the other word-level sentiment assignment approaches MacroF1score
- 40. Experimental Evaluation • We evaluated our method from three aspects using real textual datasets • Original Sentiment assignment property • Contextual sentiment assignment property
- 41. Contextual Sentiment assignment property • How accurately the sum of the contextual word-level sentiment scores in a term of each review in test dataset presents the positive or negative polarity of the review In total, we are in a bull market. R (•) :LRP + RNN (LSTM) 0.1 0.1 0.2 0.0 0.3 1.2 -0.1 0.1 + 0.1 + 0.2 + 0,0 + 0.3 + 1.2 + -0.1 = 1.8 Positive Accurate ?
- 42. Comparison Method • We compared our method with the following comparison methods • Word-level sentiment score assignment methods • PMI • FLW [D. T. Vo et. al., 2016] • SONN [Q. Li et. al., 2017] • LR: Logistic Regression • RNN with LSTM cells
- 43. Result 0.5 0.55 0.6 0.65 0.7 0.75 0.8 0.85 0.9 0.95 1 LR PMI LFW SONN LRP (Our Approach) LSTM Economic Dataset Yahoo Dataset Our method outperformed the other word-level sentiment assignment approaches (did not work better than the LSTM) MacroF1score
- 44. CCSV Example • Using CCSV, we can summarize reviews as follows Text-visualization results for a set of reviews for trading company X in September 25th, 2017 and September 30th, 2017 extracted from the Yahoo Financial Micro-blogs. Useful for decision making process in investment • Color Red: Positive Blue: Negative • Size: Volume of Sentiment
- 45. Conclusion • Summary • We propose a novel text-visualization framework called CCSV • We experimentally evaluated the validity of the CCSV • Future work • We will modify the CCSV more user-friendly • We will apply this approach to other languages
- 46. Previous work • Rule base ? • Dictionary base ?
- 47. Previous work • Rule base ? • Dictionary base ? Riles in Financial domain are too specific and specialized
- 48. To understand the financial text, we need the specialized dictionary.
- 49. Challenge • To consider both original word-level sentiment and contexts is challenging problem !
- 50. Motivation • To understand the financial text, we need • the specialized dictionary for word-level sentiment • Understand sentiment shift
- 51. Back ground • Non-experts often fail to understand financial text
- 52. Layer-wise Representation Propagation(LRP) • Calculate the relevance score of the input value to the output value by • starting from the output layer of the neural network and • backpropagating this quantity up to the input layer. market is bull Positive Negative OutputInput (L. Arras. et. al., 2017) 52
- 53. Layer-wise Representation Propagation(LRP) zj Linear Connections zg zs zj Multiplicative Connections ◎ zi wij Sigoid 関数などにより(-1,1) に変 換 53 (L. Arras. et. al., 2017)
- 54. Layer-wise Representation Propagation(LRP) zj Linear Connections zg zs zj Multiplicative Connections ◎ zi wij 54 (L. Arras. et. al., 2017)
- 55. LRP-based Approach Process • We estimate R (•) as follows 1. Develop a RNN model with LSTM cells using Text corpus dataset including document and their positive or negative sentiment tags In total, we are in a bull market. Review: Tag: Positive Text corpus dataset
- 56. Previous approach in Word-level sentiment Extraction • Previous works[Vo 2016, Li 2017] address this task by automatically developing a word sentiment score dictionary • However, they cannot consider contexts Input: In total, we are not in a bull market. In total, we are not in a bull market. Cannot consider contexts