1. Memory Standardization
Meliton Padilla

2. Overview
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3. Abstract Model the change of memory requirements for cell phones
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4. Todays standards Introduction Methodology Related work Contribution
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5. Potential Issues Original approach Noise from multiple posts
Not enough text to generate data
Limited amount of data access
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6. Product reviews Benefits Less noise Subject originated
Large sample sizes
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7. Main goal Extract feature specification from textual reviews
Target memory for multiple devices
Allow product review monitoring to inform when a change needs to be made
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8. Related work Introduction Methodology Related work Contribution
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9. Key attributes Compactness Representativeness Readability
Ganesan, K., Zhai, C., & Viegas, E. (2012, April). Micropinion generation: an unsupervised approach to generating ultra-concise summaries of opinions. InProceedings of the 21st international conference on World Wide Web (pp ). ACM.
Key attributes
Compactness
Summaries should use as few words as possible (between 2-5)
Representativeness
Summaries should reflect major opinions in text
Readability
- Summaries should be fairly well formed
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10. Micropinon
A set of short phrases expressing opinions on a specific topic or entity
Leading to a method of also creating reviews on character limited social sites
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11. Example
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12. Issues from textual anaylsis
Different types of grammar
Recreating a new sentence in order to capture original opinion (without using any original text)
How to tell the difference between a factual statement compared to an opinion
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13. solution Similarity scores: sim(mi,mj)
Measured with Jaccard similarity measure (or cosine)
Allows control redundancy of the same opinion
Readability scores: Sread(mi,mj)
- Measure well form structure of phrases (Microsoft Web N-gram)
Representativeness scores: Srep(mi,mj)
Measure how well a phrase represents the opinion from original text
Captured by a pointwise mutual information (PMI) function
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14. Example Readability scores of phrases Introduction Methodology
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15. Pang, Bo, and Lillian Lee. "Opinion mining and sentiment analysis
Pang, Bo, and Lillian Lee. "Opinion mining and sentiment analysis." Foundations and trends in information retrieval (2008):
Key attributes
Generating feature-based summaries
Distinguishing positive and negative comments
Grouping the data together to make looking for features easier
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16. Example Each summary should produce Introduction Methodology
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17. Issues How to tell if a opinion is positive or negative
Natural language processing techniques
Assuring the feature chosen is relatable to the product and not repeated
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18. Solutions Wordnet Part-of-Speech Tagging (POS)
System that helps find opinion words and frequent features
Part-of-Speech Tagging (POS)
Frequency of nouns, verb, adjective, etc. (Nlprocessor linguistic parser)
Orientation identification for opinion words
- Only positive and negative orientations
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19. Example
Using Wordnet to create a positive/negative approach a bipolar cluster
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20. Methodology Introduction Methodology Related work Contribution
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21. Key differences Focus just on the memory features of a device
Include other electronic devices besides just cell phones, examples such as laptops, mp3s and cameras
Sample current and past reviews to see if a trend can be modeled from the data
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22. Processing techniques
Product reviews and previous data sets
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23. Processing techniques
Data is filtered
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24. Processing techniques
Steps needed
Collect large amount of data (may be separated by product type)
Extract opinion sentences and sort into a positive/negative category
Keep count of the positive to negative ratio
Use a similarity technique to measure the sweet spot of minimum required memory, in order to have a good product
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25. Processing techniques
Potential issues
Getting current reviews from Amazon
Currently provided API to view a current URL review page for 24hours
Comparing different products based on memory capability's
Analyzing textual data
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26. Contribution
Being able to provide a way for consumers or manufacturers an easy method to decide on the memory required
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27. Questions?

28. References
[1] Pang, B., & Lee, L. (2008). Opinion mining and sentiment analysis.Foundations and trends in information retrieval, 2(1-2),
[2] Ganesan, Kavita. "Micropinions vs. Micro-reviews." Text Mining, Analytics & More:. N.p., n.d. Web. 12 Oct
[3] Ganesan, K., Zhai, C., & Viegas, E. (2012, April). Micropinion generation: an unsupervised approach to generating ultra-concise summaries of opinions. InProceedings of the 21st international conference on World Wide Web (pp ). ACM.
[4] Qadir, A. (2009, September). Detecting opinion sentences specific to product features in customer reviews using typed dependency relations. InProceedings of the Workshop on Events in Emerging Text Types (pp ). Association for Computational Linguistics