def analysis(q, query, window, width, height):
tweetDict = miner(query)
for keys in tweetDict:
tweetDict[keys] = stopwordRemover(tweetDict[keys], query)
tweetDict = duplicateRemover(tweetDict)
wordCount = countWords(tweetDict)
sentimentDict = sentimentPreprocess()
wordCount = (sorted(wordCount.items(), key=lambda item: item[1]))[-30:]
tfidfScore = {}
for key in wordCount:
score = tfidf(key[0], key[1])
tfidfScore[key[0]] = (score)
keywordList = sorted(tfidfScore.items(), key=lambda item: item[1])[-15:]
print keywordList
queryKeywordRelationship = {}
for word in keywordList:
queryKeywordRelationship[word[0]] = sentimentComparison(tweetDict, query, word[0], sentimentDict)
relationshipList = []
for pair in itertools.combinations(keywordList, 2):
if sentimentComparison(tweetDict, pair[0][0], pair[1][0], sentimentDict) != 0:
relationshipList.append((pair[0][0], pair[1][0], sentimentComparison(tweetDict, pair[0][0], pair[1][0], sentimentDict)))
particleDict = massSpringModel(relationshipList, window, width, height)
twitterData = {}
twitterData['particles'] = particleDict
twitterData['querykeywords'] = queryKeywordRelationship
q.put(twitterData)
def getdetail(self): item_id = self.ItemEntry.get() m = miner() m.getItemDetail(item_id) return
def getcats(self): m = miner() m.getCategories()
def getitems(self): seller_id = self.SellerEntry.get() cat_id = self.CatEntry.get() days = int(self.DaysEntry.get()) m = miner() m.getSellerItems(seller_id, cat_id, days)
def gettrans(self): item_id = self.ItemEntry.get() m = miner() m.getItemTrans(item_id) return
