Agro-Produce Marketing Using Social Network

Agro-Produce Marketing Using Social Network Mehul Patel

Sanjay Chaudhary

Minal Bhise

DA-IICT Nr. Indroda Circle, Nr. GH-0, Gandhinagar, Gujarat, India



[email protected] [email protected]

ABSTRACT Microblogging services are part of social network platforms, which allow people to exchange short messages. Social networks provide people to play an active role in collecting, analyzing and reporting news and information. People can use social network platform for marketing, buying and selling of their products. A sellers can tweet regarding product information including links of related photos, videos etc. A buyer can show interest in the product by means of tweets. Social network can be used as a mechanism to bring sellers and buyers closer. It provides a common platform for buyers and sellers to sell and buy their products. Microblogs can be parsed and analyzed to generate useful suggestions, e.g. sellers can be informed about potential buyers to get higher profit. Such information can be used to generate classified information to help users to take decision, e.g. minimum expected price of a crop that sellers expect in a given region. Microblogs can be written in different regional languages. Agro-produce marketing information can be processed and then stored in RDF/RDF(S) and OWL data store. SPARQL and conjunctive queries or SPARQL-DL can be used to generate classified summarized information from RDF/RDF(S) and OWL data store. This paper proposes the use of social network platform like twitter to enable trade interactions among farmers and merchants, provide suggestions and recommendations based on classified summarized information using database, RDF/RDF(S) and OWL for better decision making.

General Terms Design, Experimentation

Keywords Social Network, Agro-produce marketing, OWL, RDF, RDF(S), Semantic Web

1. INTRODUCTION Microblogging sites like Twitter are social network platforms, which allow people to exchange short messages. Social network allows people to play an active role in creating, analyzing, collecting and reporting news and information [1]. People can

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also use social networks platform for marketing, selling and buying of their products. Sellers can tweet to describe their product(s) using text, links to photos, videos etc. Interested buyers can show their interest into the product(s) by means of tweets. The tweets can then be processed and suggestions can be sent back to the user. Social network gives sellers and buyers platform to buy and sell their products. Social networks like Twitter can also be accessed via text messages. [2] The collected tweets from sellers and buyers can be used to generate classified summarized information, which can can be useful to farmers, buyers and government. Illustration of queries related to relevant information is shown as under: 1.

What is the maximum, minimum, average price of the product expected by sellers in a specific region?

2.

What is the maximum, minimum, average price buyers are willing to pay for a particular product in a specific region?

3.

Where should sellers sell to get buyers who are ready to pay more than average expectation of sellers?

4.

From where buyers should buy to get sellers, who expect less than average expectation of buyers?

As microblogging sites are supporting Unicode characters, people can blog in their regional language. To generate classified summarized information and to bring sellers and buyers close to each other, cross language processing is required.

2. USE OF TWEETS 2.1 Hashtag Twitter1 does not provide any means of grouping tweets together. Twitter community has created hashtags. It provides the way to group tweets containing some hashtag. Hashtag contains #word and it will be grouped to the other tweets containing same #word. If user clicks the hashtag word then twitter shows all the tweets in the same hashtag category. Hashtags can be added anywhere in the tweets. Frequently used hashtags are defined as trending topic by twitter [1], [2]. Example: “mehulpatel #sell I want to sell 100 kilograms of wheat of type `Lokvan' at Rs. 23 per kilogram” Above example shows that #sell is a hashtag and it groups all the tweets which uses #sell hashtag. This hashtag can be used anywhere in tweet.

1

http://www.twitter.com

2.2 Assumptions about Tweet Structure Central User is created on Twitter with name "APBroker2". Twitter allows fetching of tweets in real time mode with user stream APIs. All the sellers and buyers will follow central user. APIs require authentication key for user account. Central user is authenticated to get the key and it is used to send direct messages as well as fetching of all the tweets sent to it. Following are the assumptions regarding tweet structure. 1. Sellers’ tweets are based on following structure. Unit of measurement is considered as 20 kilograms.

XML (eXtensible Markup Language), RSS (Really Simple Syndication) and ATOM formats. Tweets have structure associated with it. Each tweet is associated with user and based on this information; user’s location information is gathered from user’s profile. For this purpose GeoPlanet2 APIs are used. This information is stored at a central repository. Analysis phase uses data from central repository to generate classified information. Generated information will be displayed on user interface. Data will be converted into RDF3, RDF(S)4 and OWL5 to provide semantics. SPARQL6, Pallet7 and SPARQL-DL8 are used to reason from these files.

1.1 @CentralUser #AgroSell Product type, Product subtype, Minimum price per unit, Quantity, Date Example: Farmer: @CentralUser #AgroSell Wheat, Lokvan, 500, 100, 2011-11-12 Above example shows that farmer wants to sell 100 units of wheat of type `Lokvan' at the rate of Rs. 500 per unit by the date 201111-12. 2. Buyers’ tweets are based on following structure. 2.1 @CentralUser #InterestedIn Product type Example: @CentralUser #InterestedIn Wheat Above example shows that buyer is interested in getting notification regarding agro-produce of type wheat. Comma separated list can be used at product type to show interest in more than one agro-produce. 2.2 @CentralUser #AgroBuy Seller, Agro-produce id, Product type, Product subtype, Price per unit, Quantity Example: @CentralUser #AgroBuy Wheat, Lokvan, 600, 100

Farmer,

1,

Above example shows that buyer wants to buy 100 units of wheat of type Lokvan at the rate of 600 per unit from the farmer ‘Farmer’. Messages that will be delivered to interested buyers have following format. 2.3 Farmer #AgroSell Product type, Product subtype, Minimum Price per unit, Quantity, Date, Agro-produce id Example: Farmer #AgroSell ઘઊં, શિરોરી, 340, 60, 18-01-2011, 10 2.4 Buyer #AgroBuy Seller, Agro-produce id, Product type, Product subtype, Price per unit, Quantity Example: Merchant #AgroBuy Farmer, 8, ઘઊં, ટુ કડી, 300, 50 3. Suggestions to the farmers are based on following structure: 3.1 #ShownInterest Buyer, Rank, Product type, Product subtype, Price per unit, Quantity Example: #ShownInterest

Merrchant1,

1,

Figure 1. Architecture.

4. IMPLEMENTATION SCENARIO AND STRATEGY Figure 2 shows interaction among buyers and sellers. All the sellers and buyers need to follow a central user, called a broker. Interested buyers can register themselves with the central user to get notification regarding product(s) of interest. Sellers send tweets containing product information and deadline by which they would like to sell the product(s). This information will be sent to the registered buyers in the form of direct message(s). Buyers can show interest in product(s) by means of tweets. This information will be sent to other interested buyers. When deadline mentioned by seller is exhausted, suggestion(s) to seller will be sent in the form of direct message(s). Suggestions indicate buyers, ready to pay higher prices.

4.1 USER ENGAGEMENT Users are expected to create accounts on twitter.com and provide their location information in their profile. To leverage the functionality presented in this paper, users need to follow a central user, called a broker. Sellers and buyers can interact by

ઘઊં,

લોકવન, 580, 50

2

http://developer.yahoo.com/geo/geoplanet/

We assume that location in user’s profile describes geographical information.

3

http://www.w3.org/RDF/

4

http://www.w3.org/TR/rdf-schema/

3. ARCHITECTURE

5

http://www.w3.org/TR/owl-features/

Figure 1 shows architecture of the system. Tweets are fetched from the social network like twitter. Fetching process uses Twitter APIs e.g. REST (REpresenatational State Transfer) and streaming APIs. APIs return tweets in JSON (JavaScript Object Notation),

6

http://www.w3.org/TR/rdf-sparql-query/

7

http://clarkparsia.com/pellet/

8

http://www.derivo.de/en/resources/sparql-dl-api.html

sending tweets in proper format as mentioned in section 2.2 to the central user. Sellers and buyers can use text messages, social network access facility provided on mobile phones, or web applications to send and receive tweets. After receiving suggestion(s), sellers can contact the potential buyers. In future, sellers, buyers and other government agencies will be provided facility to use web application/services for fetching classified summarized information for better decision making.

Figure 2. Interactions among Buyers and Sellers.

5. CLASSIFIED SUMMARIZED INFORMATION Based on data gathered at the central repository, following classified information can be generated for farmers, merchants and government: 1.

Min/max/average price of a given product, at a given place, for a given period for buyers and/or sellers.

2.

Min/max/ average price of each of products at each location (town, state, country) for a given period for buyers and/or sellers.

3.

Suggestions like in which region sellers should sell to get byers ready to pay more than average expectation of sellers.

4.

Suggestions like from which region buyers should buy to get sellers who expect less than average expectation of buyers.

5.

What percentage of farmers and buyers are negotiating for a specific crop in a specific region (town, state, country)?

6.

What farmers should grow to have a large number of buyers?

5.1 Use of Principles of Semantic Web Semantic web components like RDF/RDF(S) and OWL provides metadata, reusability using data integration, interoperability and information exchange [3]. Information stored in database can be converted in RDF/RDF(S) using tool like D2R [4] and in OWL using tool like DataMaster [5] to associate semantics with the data. D2R currently does not support converting schema of database to RDF(S) [4]. It can be done using code. SPARQL can be used to reason from RDF/RDF(S) and conjunctive queries [3] and SPARQL-DL can be used to reason from OWL.

6. RELATED WORK Twitris9 does spatio-temporal-thematic analysis of citizen observations, which are tweeted on Twitter, pertaining to real world events. It collects tweets at regular interval and it uses trending terms from Google insight for Search API10. These tweets are partitioned into groups by first considering space aspect of tweets and groups are again partitioned based on time of the tweets. The goal is to get theme from these tweets. Nouns are better representation of events. Named entity recognizer is used to get nouns from tweets. N-gram11 (3-gram in Twitris) analysis is performed to find TF-IDF12 (term frequency inverse domain frequency) of n-gram. Top 5 n-grams are picked for further analysis and using them it collects tweets again from Twitter. These top n-grams are called event descriptors, used to get relevant information from Google news, Wikipedia and are shown on GUI. On selection of event descriptor, GUI shows tweets related to an event on Google Map. Time can be selected using calendar to see events for a specific day. On selection of space, Twitris fetches tweets related to event descriptor, Google news and Wikipedia articles and displays them [1]. Social network site like Facebook13 can be used to create virtual store by any organic food producer using Facebook page facility to expand and communicate with the customers. By posting photos, notes, links or videos related to business, organic food producers can market their product(s). If Facebook users become fan of created branded pages then they will be notified when branded page’s administrator updates on page via primary news feed on the homepage. Organic food producer can use Facebook’s basic features/applications like wall, info, notes, reviews, discussions and photos etc. for effective marketing and allowing users to share information and provide reviews related to product(s). The other benefits of using Facebook pages are branding, customer engagement, new customer acquisition, customer retention, drive web traffic, feedback mechanism and reputation management. Most of Facebook users are from young generation and they are usually not decision makers within their family and delivery distance [6].

7. IMPLEMENTATION Agro-Produce marketing domain is considered as a proof of concept. To implement this scenario on twitter, different accounts are created, i.e. a central user, farmers and merchants. Java is used as a programming language. MySQL is the database and Netbeans is the development environment. Figure 3 shows various modules for the implementation. `Tweet fetch and send module' is responsible for fetching tweets from the central user. User stream APIs are used for this purpose with Twitter4J14 library. It sends direct messages to interested buyers using twitter REST APIs15 and fetches user’s geographic information using GeoPlanet APIs. All of the collected data is stored in a database. `Farmer 9

http://twitris.knoesis.org/

10

http://www.google.com/insights/search/

11

http://en.wikipedia.org/wiki/N-gram

12

http://en.wikipedia.org/wiki/Tf%E2%80%93idf

13

http://www.facebook.com

14

http://twitter4j.org

15

http://apiwiki.twitter.com/w/page/22554679/Twitter-APIDocumentation

suggestion module' is responsible for giving suggestion(s) to the farmers based on deadline indicated by them in their tweets. Central repository of database is converted to RDF/RDF(S) by a program written in java and in OWL using DataMaster [5] tool to represent tweet data. Analysis module is responsible for generating and displaying classified summarized information as described in section 5. Stored procedures are used to process data from database and SPARQL is used to fetch from RDF/RDF(S). Jena APIs16 are used to execute SPARQL against RDF/RDF(S) data store. SPARQL-DL queries are used to reason from OWL. It is a query engine on the top of OWL APIs17 to reason from OWL data store. Casper dataset18 is used in OWL reasoning algorithms to achieve various functionalities such as group by, filtering, sorting etc.

There is a need to build domain specific ontology to have regional support that provides semantic. Currently available ontologies are in English, but ontologies in regional languages are required for regional language support. OpenLink Virtuoso20 supports unlimited storage for RDF (Resource Description Framework) and it can be queried using SPARQL. It currently does not support owl:equivalenceclass for inferencing [7]. Ontology can be used with OpenLink Virtuoso, provided it supports owl:equivalenceclass for inferencing. GoodRelation21 ontology can be used with twitter provided twitter extends annotation limit. This ontology can be used for describing seller’s company, store, product(s) information and buyer’s requirement. There is also a need for a portal, where users can login using twitter account and access classified summarized information. Sellers and buyers can rate each other in terms of quality of service, response time etc. Major trading locations can be displayed according to product types using map. Information can be updated according to current trading activities. Trust among buyers and sellers are essential to implement real business transactions. Feedback regarding quality of products, delivery time, breach of business deal etc. can be provided to build reputation of sellers and buyers. We aim to build modules to incorporate trust and feedback.

9. ACKNOWLEDGEMENT Authors would like to thank Dr. Amit Sheth, (Professor and Director, knoesis research center, Ohio Center of Excellence in Knowledge-enabled Computing, Wright State University, USA) and his group members for their continuous support and guidance.

10. REFERENCES Figure 3. Implementation.

8. Conclusion and Future work Social networking platform such as twitter.com can be used for agro-produce marketing. Twitter is accessible by text messages and using smart phones. It provides platform to farmers to sell their product(s) and merchants to buy agro-produce and re-sell in the market. Generated classified summarized information can be used by farmers, buyers and government. This approach can be extended to have multiple hashtags to indicate different domains. Currently marketing of agro-produce domain is considered. If more than one hashtag is used then another domain such as electronic items etc. can be considered for effective decision making. Following example shows that seller wants to sell camera of cannon company at Rs. 5000 by date 12/11/2011. Example: Seller: #sell #electronics canon, 5000, 1, 12/11/2011

camera,

Gujarati language can be supported as a regional language provided Gujarati lexicon19 can provide APIs for language translation. This APIs can be used for translation between Gujarati and another language like English during analysis phase.

[1] Nagarajan, M., Gomadam, K., Sheth, A.P., Ranabahu, A., Mutharaju, R., and Jadhav A. 2009. Spatio-Temporal-Thematic Analysis of Citizen Sensor Data : Challenges and Experiences. Proceedings of the 10th International Conference on Web Information Systems Engineering. 2009, 539-553. DOI: http://dx.doi.org/10.1007/978-3-642-04409-0_52 [2] Fitton, L., Gruen, M. and Poston, L. P. 2009. Twitter for Dummies. Wiley Publishing, Inc. [3] Hitzler, P., Krötzsch, M. and Rudolph, S. P. 2009. Foundations of Semantic Web Technologies. Chapman & Hall/CRC. [4] D2R MAP - Database to RDF Mapping Language and Processor. As accessed on, 12 December 2010. [Online]. Available: http://www4.wiwiss.fuberlin.de/bizer/d2rmap/D2Rmap.htm [5] DataMaster. As accessed on, 02 January 2011. [Online]. Available: http://protegewiki.stanford.edu/wiki/DataMaster [6] Pertiwi, S. 2010. Expanding Farm Business through Popular Social Network Site. AFITA 2010 International Conference, The Quality Information for Competitive Agricultural Based Production System and Commerce. 2010. [7] Virtuoso 6 FAQ. As accessed on, 5 November 2010. [Online]. Available: http://www.openlinksw.com/dataspace/dav/wiki/Main/VOSVirtuo so6FAQ

16

http://jena.sourceforge.net/

17

http://owlapi.sourceforge.net/

18

http://code.google.com/p/casperdatasets/

20

http://virtuoso.openlinksw.com/

http://www.gujaratilexicon.com/

21

http://www.heppnetz.de/projects/goodrelations

19