├── README.md ├── data ├── negative.json ├── perilipsi_tweets.sql ├── positive.json └── unigrams-pmilexicon.txt ├── main.py └── src ├── __init__.py ├── datalink.py ├── features.py ├── hashtags.py └── sentiment_plot.py /README.md: -------------------------------------------------------------------------------- 1 | Sentiment Analyzer 2 | ================== 3 | 4 | A Python module to do a set of operations on tweets. It uses a collection of stopwords to train a dataset for the sentiment analysis. It uses the basic principle of bag-of-words used for natural language processing. 5 | 6 | Dependencies 7 | ------------ 8 | * numpy 9 | * matplotlib(To plot sentiments) 10 | 11 | How to use 12 | ---------- 13 | ```python 14 | from src import features, datalink, hashtags 15 | import time 16 | dblink = datalink.DatabaseConnectionDown('perilipsi_tweets') 17 | emoTest = features.Emoticons() 18 | dictTest = features.DictionaryTest() 19 | hashtest = hashtags.hashtags() 20 | testTweet, tweetTime = dblink.fetchTweet()['tweet'], dblink.fetchTweet()['time'] #You can pass anything you want 21 | emo_test = emoTest.analyse(testTweet) 22 | dict_test = dictTest.analyse(testTweet) 23 | hash_test = hashtest.analyseHashtagTweet(testTweet) 24 | print "Emoticons:", emo_test 25 | print "DictionaryTest:", dict_test 26 | print "Hashtags: ", hash_test 27 | ``` 28 | Output 29 | ```python 30 | Emoticons: {'positive': 0.33, 'negative': 0.66} 31 | DictionaryTest: {'positive': 0.46153846153846156, 'negative': 0.5384615384615384} 32 | Hashtags: {'positive': 0.38, 'negative': 0.62} 33 | ``` 34 | Progress 35 | -------- 36 | * Emoticons: This class uses emoticons detection to classify the passed string as positive or negative 37 | * DictionaryTest: This class uses a set of English words and their subjectivity to give a score to a string 38 | * hashtags: This class extracts hashtags from the string sent and calculates the sentiment based on a trained dataset 39 | * AllCaps 40 | * ElongatedWords 41 | * Negation 42 | * Punctuation 43 | 44 | Social Network APIs 45 | --------------- 46 | * Twitter Search API 47 | * Facebook Graph API 48 | 49 | Computation Engines 50 | ------------------- 51 | Wolfram Alpha 52 | 53 | Team Members 54 | ------------ 55 | 56 | 57 | 58 | 59 |
NameEmail
Sudhanshu Mishra mrsud94@gmail.com
Ambar Mehrotraambar.prince@gmail.com
60 | 61 | 62 | [![Bitdeli Badge](https://d2weczhvl823v0.cloudfront.net/mrsud/sentiment-analyzer/trend.png)](https://bitdeli.com/free "Bitdeli Badge") 63 | 64 | -------------------------------------------------------------------------------- /data/negative.json: -------------------------------------------------------------------------------- 1 | { 2 | "FALSE": "strong", 3 | "abandon": "weak", 4 | "abandoned": "weak", 5 | "abandonment": "weak", 6 | "abase": "strong", 7 | "burdened": "strong", 8 | "burden": "strong", 9 | "abasement": "strong", 10 | "abash": "strong", 11 | "abate": "weak", 12 | "abdicate": "weak", 13 | "aberration": "strong", 14 | "abhor": "strong", 15 | "abhorred": "strong", 16 | "abhorrence": "strong", 17 | "abhorrent": "strong", 18 | "abhorrently": "strong", 19 | "abhors": "strong", 20 | "abject": "strong", 21 | "abjectly": "strong", 22 | "abjure": "weak", 23 | "abnormal": "weak", 24 | "abolish": "weak", 25 | "abominable": "strong", 26 | "abominably": "strong", 27 | "abominate": "strong", 28 | "abomination": "strong", 29 | "abrade": "weak", 30 | "abrasive": "strong", 31 | "abrupt": "weak", 32 | "abscond": "strong", 33 | "absence": "weak", 34 | "absentee": "weak", 35 | "absentminded": "strong", 36 | "absurd": "strong", 37 | "absurdity": "strong", 38 | "absurdly": "strong", 39 | "absurdness": "strong", 40 | "abuse": "strong", 41 | "abuses": "weak", 42 | "abusive": "strong", 43 | "abysmal": "strong", 44 | "abysmally": "strong", 45 | "abyss": "strong", 46 | "accidental": "weak", 47 | "accost": "weak", 48 | "accountable": "weak", 49 | "accursed": "strong", 50 | "accusation": "strong", 51 | "accusations": "strong", 52 | "accuse": "strong", 53 | "accuses": "strong", 54 | "accusing": "strong", 55 | "accusingly": "strong", 56 | "acerbate": "strong", 57 | "acerbic": "strong", 58 | "acerbically": "strong", 59 | "ache": "strong", 60 | "acrid": "strong", 61 | "acridly": "strong", 62 | "acridness": "strong", 63 | "acrimonious": "strong", 64 | "acrimoniously": "strong", 65 | "acrimony": "strong", 66 | "adamant": "strong", 67 | "adamantly": "strong", 68 | "addict": "weak", 69 | "addiction": "weak", 70 | "admonish": "strong", 71 | "admonisher": "strong", 72 | "admonishingly": "strong", 73 | "admonishment": "strong", 74 | "admonition": "strong", 75 | "adrift": "weak", 76 | "adulterate": "strong", 77 | "adulterated": "strong", 78 | "adulteration": "strong", 79 | "adversarial": "weak", 80 | "adversary": "weak", 81 | "adverse": "weak", 82 | "adversity": "strong", 83 | "affectation": "weak", 84 | "afflict": "weak", 85 | "affliction": "weak", 86 | "afflictive": "strong", 87 | "affront": "strong", 88 | "afraid": "strong", 89 | "against": "weak", 90 | "aggravate": "strong", 91 | "aggravating": "strong", 92 | "aggravation": "strong", 93 | "aggression": "strong", 94 | "aggressive": "strong", 95 | "aggressiveness": "strong", 96 | "aggressor": "strong", 97 | "aggrieve": "strong", 98 | "aggrieved": "strong", 99 | "aghast": "strong", 100 | "agitate": 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"zealot": "strong", 4165 | "zealous": "strong", 4166 | "zealously": "strong" 4167 | } 4168 | -------------------------------------------------------------------------------- /data/perilipsi_tweets.sql: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/debugger22/sentiment-analyzer/b7ec918cfacf37eb31af8ee46a2ee9cd6c903980/data/perilipsi_tweets.sql -------------------------------------------------------------------------------- /data/positive.json: -------------------------------------------------------------------------------- 1 | { 2 | "TRUE": "strong", 3 | "abidance": "strong", 4 | "abide": "strong", 5 | "abilities": "weak", 6 | "ability": "weak", 7 | "able": "weak", 8 | "abound": "weak", 9 | "above": "weak", 10 | "aboveaverage": "weak", 11 | "absolve": "strong", 12 | "abundance": "weak", 13 | "abundant": "weak", 14 | "accede": "strong", 15 | "accept": "weak", 16 | "acceptable": "weak", 17 | "acceptance": "weak", 18 | 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"understate": "strong", 2139 | "understated": "strong", 2140 | "understatedly": "strong", 2141 | "understood": "strong", 2142 | "undisputable": "strong", 2143 | "undisputably": "strong", 2144 | "undisputed": "weak", 2145 | "undoubted": "strong", 2146 | "undoubtedly": "strong", 2147 | "unencumbered": "weak", 2148 | "unequivocal": "strong", 2149 | "unequivocally": "strong", 2150 | "unfazed": "strong", 2151 | "unfettered": "strong", 2152 | "unforgettable": "strong", 2153 | "uniform": "weak", 2154 | "uniformly": "weak", 2155 | "unique": "weak", 2156 | "unity": "weak", 2157 | "universal": "weak", 2158 | "unlimited": "weak", 2159 | "unparalleled": "strong", 2160 | "unpretentious": "strong", 2161 | "unquestionable": "strong", 2162 | "unquestionably": "strong", 2163 | "unrestricted": "weak", 2164 | "unscathed": "weak", 2165 | "unselfish": "strong", 2166 | "untouched": "weak", 2167 | "untrained": "weak", 2168 | "upbeat": "strong", 2169 | "upfront": "weak", 2170 | "upgrade": "weak", 2171 | "upheld": "strong", 2172 | "uphold": "strong", 2173 | "uplift": "weak", 2174 | "uplifting": "strong", 2175 | "upliftingly": "strong", 2176 | "upliftment": "strong", 2177 | "upright": "weak", 2178 | "upscale": "weak", 2179 | "upside": "strong", 2180 | "upward": "weak", 2181 | "urge": "strong", 2182 | "usable": "weak", 2183 | "useful": "weak", 2184 | "usefulness": "weak", 2185 | "utilitarian": "weak", 2186 | "utmost": "strong", 2187 | "uttermost": "strong", 2188 | "valiant": "strong", 2189 | "valiantly": "strong", 2190 | "valid": "weak", 2191 | "validity": "weak", 2192 | "valor": "strong", 2193 | "valuable": "strong", 2194 | "value": "weak", 2195 | "values": "weak", 2196 | "vanquish": "strong", 2197 | "vast": "weak", 2198 | "vastly": "weak", 2199 | "vastness": "weak", 2200 | "venerable": "strong", 2201 | "venerably": "strong", 2202 | "venerate": "weak", 2203 | "verifiable": "weak", 2204 | "veritable": "strong", 2205 | "versatile": "weak", 2206 | "versatility": "weak", 2207 | "viability": "weak", 2208 | "viable": "weak", 2209 | "vibrant": "strong", 2210 | "vibrantly": "strong", 2211 | "victorious": "weak", 2212 | "victory": "weak", 2213 | "vigilance": "weak", 2214 | "vigilant": "weak", 2215 | "vigorous": "weak", 2216 | "vigorously": "weak", 2217 | "vindicate": "strong", 2218 | "vintage": "weak", 2219 | "virtue": "strong", 2220 | "virtuous": "strong", 2221 | "virtuously": "strong", 2222 | "visionary": "strong", 2223 | "vital": "strong", 2224 | "vitality": "strong", 2225 | "vivacious": "strong", 2226 | "vivid": "strong", 2227 | "voluntarily": "weak", 2228 | "voluntary": "weak", 2229 | "vouch": "strong", 2230 | "vouchsafe": "strong", 2231 | "vow": "strong", 2232 | "vulnerable": "strong", 2233 | "want": "strong", 2234 | "warm": "weak", 2235 | "warmhearted": "strong", 2236 | "warmly": "strong", 2237 | "warmth": "weak", 2238 | "wealthy": "weak", 2239 | "welcome": "weak", 2240 | "welfare": "weak", 2241 | "well": "weak", 2242 | "wellbeing": "strong", 2243 | "wellconnected": "strong", 2244 | "welleducated": "weak", 2245 | "wellestablished": "weak", 2246 | "wellinformed": "strong", 2247 | "wellintentioned": "strong", 2248 | "wellmanaged": "strong", 2249 | "wellpositioned": "weak", 2250 | "wellpublicized": "weak", 2251 | "wellreceived": "strong", 2252 | "wellregarded": "strong", 2253 | "wellrun": "strong", 2254 | "wellwishers": "strong", 2255 | "whimsical": "strong", 2256 | "white": "weak", 2257 | "wholeheartedly": "strong", 2258 | "wholesome": "strong", 2259 | "wide": "weak", 2260 | "wideopen": "weak", 2261 | "wideranging": "weak", 2262 | "will": "strong", 2263 | "willful": "strong", 2264 | "willfully": "strong", 2265 | "willing": "strong", 2266 | "willingness": "strong", 2267 | "wink": "strong", 2268 | "winnable": "strong", 2269 | "winners": "strong", 2270 | "wisdom": "strong", 2271 | "wise": "strong", 2272 | "wisely": "strong", 2273 | "wish": "strong", 2274 | "wishes": "strong", 2275 | "wishing": "strong", 2276 | "witty": "strong", 2277 | "wonder": "strong", 2278 | "wonderful": "strong", 2279 | "wonderfully": "strong", 2280 | "wonderous": "strong", 2281 | "wonderously": "strong", 2282 | "wondrous": "strong", 2283 | "woo": "strong", 2284 | "workable": "strong", 2285 | "worldfamous": "strong", 2286 | "worship": "weak", 2287 | "worth": "strong", 2288 | "worthiness": "strong", 2289 | "worthwhile": "strong", 2290 | "worthy": "strong", 2291 | "wow": "strong", 2292 | "wry": "strong", 2293 | "yearn": "strong", 2294 | "yearning": "strong", 2295 | "yearningly": "strong", 2296 | "yep": "strong", 2297 | "yes": "strong", 2298 | "youthful": "weak", 2299 | "zeal": "strong", 2300 | "zenith": "strong", 2301 | "zest": "strong" 2302 | } -------------------------------------------------------------------------------- /main.py: -------------------------------------------------------------------------------- 1 | from src import features, datalink, hashtags, sentiment_plot 2 | import time 3 | dblink = datalink.DatabaseConnectionDown('perilipsi_tweets') 4 | plotter = sentiment_plot.Plotter() 5 | emoTest = features.Emoticons() 6 | dictTest = features.DictionaryTest() 7 | hashtest = hashtags.hashtags() 8 | # You can pass anything you want 9 | testTweet, tweetTime = dblink.fetchTweet( 10 | )['tweet'], dblink.fetchTweet()['time'] 11 | emo_test = emoTest.analyse(testTweet) 12 | dict_test = dictTest.analyse(testTweet) 13 | hash_test = hashtest.analyseHashtagTweet(testTweet) 14 | print "Emoticons:", emo_test 15 | print "DictionaryTest:", dict_test 16 | print "Hashtags: ", hash_test 17 | -------------------------------------------------------------------------------- /src/__init__.py: -------------------------------------------------------------------------------- 1 | from features import Emoticons, DictionaryTest 2 | from datalink import DatabaseConnectionDown -------------------------------------------------------------------------------- /src/datalink.py: -------------------------------------------------------------------------------- 1 | import MySQLdb 2 | import random 3 | import time 4 | 5 | 6 | class DatabaseConnectionDown: 7 | 8 | def __init__(self, adb): 9 | ''' 10 | This initializes the Database Connection for downlink 11 | @param DatabeseName 12 | ''' 13 | self.adb = adb 14 | try: 15 | self.db = MySQLdb.connect( 16 | user='root', 17 | db=adb, 18 | passwd='adminpass', 19 | host='localhost') 20 | except MySQLdb.Error as e: 21 | print "An error has been raised. %s" % e 22 | 23 | def fetchTweet(self): 24 | ''' 25 | This method returns a dictionary of tweet and its timestamp from the Database 26 | DictKeys: 'tweet', 'time' 27 | ''' 28 | randomNo = random.randint(1, 10000) 29 | with self.db: 30 | cur = self.db.cursor() 31 | query = "Select `text`, `created_at` from `" + self.adb + \ 32 | "`.`tweet` where `iso_language` ='en' LIMIT " + repr(randomNo) + ", " + repr(randomNo + 1) 33 | cur.execute(query) 34 | temp = cur.fetchone() 35 | return {'tweet': temp[0], 'time': int( 36 | time.mktime(time.strptime(str(temp[1]), '%Y-%m-%d %H:%M:%S')))} 37 | 38 | def fetchTweets(self, limit): 39 | ''' 40 | This method returns a dictionary of list of tweets and list of timestamps from the Database 41 | DictKeys: 'tweets', 'time' 42 | ''' 43 | with self.db: 44 | cur = self.db.cursor() 45 | query = "Select `text`, `created_at` from `" + self.adb + \ 46 | "`.`tweet` where `iso_language` ='en' LIMIT 0," + repr(limit) 47 | cur.execute(query) 48 | temp = cur.fetchall() 49 | tweets = [] 50 | timestamps = [] 51 | for i in temp: 52 | tweets.append(i[0]) 53 | timestamps.append( 54 | int(time.mktime(time.strptime(str(i[1]), '%Y-%m-%d %H:%M:%S')))) 55 | return {'tweets': tweets, 'time': timestamps} 56 | -------------------------------------------------------------------------------- /src/features.py: -------------------------------------------------------------------------------- 1 | import re 2 | import json 3 | 4 | 5 | class Emoticons: 6 | 7 | ''' 8 | This class uses emoticons detection to classify the passed string as positive or negative 9 | ''' 10 | 11 | def analyse(self, string): 12 | ''' 13 | This method takes a string as parameter and returns a dictionary of 14 | probabilities of sentiment of the string 15 | keys: 'positive', 'negative' 16 | If the test fails, both the keys will have value equal to 0 17 | ''' 18 | self.string = re.sub( 19 | r'\W+:\)\(\'\{\}\-\@\>\<\=\;\[\]\!', 20 | ' ', 21 | string).lower() 22 | self.string = self.string.replace('.', '') 23 | self.string = self.string.replace('?', '') 24 | self.words = self.string.split(" ") 25 | if self.words[-1] == '': # how will something to to the -1 index? 26 | del self.words[-1] 27 | positiveEmoz = [ 28 | ':)', 29 | ':-)', 30 | ':D', 31 | ':-D', 32 | ':P', 33 | ':-P', 34 | ';)', 35 | ';-)', 36 | ';D', 37 | ';-D', 38 | ':o)', 39 | ':]', 40 | ':3', 41 | ':c)', 42 | ':>', 43 | '=]', 44 | '8)', 45 | '=)', 46 | ':}', 47 | '8D', 48 | 'xD', 49 | 'XD', 50 | 'X-D', 51 | '=D', 52 | '=3', 53 | ':-))', 54 | ':\')', 55 | 'lol', 56 | 'lol!'] 57 | negativeEmoz = [ 58 | ':(', 59 | ':-(', 60 | ':(', 61 | ':-(', 62 | ':-<', 63 | ':-[', 64 | ':[', 65 | ':{', 66 | ':-||', 67 | ':@', 68 | ':\'-(', 69 | ':\'(', 70 | 'QQ', 71 | 'D:', 72 | 'D:<', 73 | 'D8', 74 | 'D;', 75 | 'DX', 76 | 'v.v', 77 | '>.<', 78 | 'D='] 79 | positiveCount = 0 80 | negativeCount = 0 81 | for i in self.words: 82 | if i in positiveEmoz: 83 | positiveCount += 1 84 | if i in negativeEmoz: 85 | negativeCount += 1 86 | positiveEmoz, negativeEmoz = 0, 0 87 | if positiveCount + negativeCount == 0: 88 | return {'positive': 0, 'negative': 0} 89 | return {'positive': float(positiveCount) / 90 | (positiveCount + 91 | negativeCount), 'negative': float(negativeCount) / 92 | (positiveCount + 93 | negativeCount)} 94 | 95 | 96 | class DictionaryTest: 97 | 98 | ''' 99 | This class uses a set of English words and their subjectivity to give a score to a string 100 | ''' 101 | 102 | def analyse(self, string): 103 | ''' 104 | This method takes a string as parameter and returns a dictionary of 105 | probabilities of sentiment of the string 106 | keys: 'positive', 'negative' 107 | If the test fails, both the keys will have value equal to 0 108 | ''' 109 | self.string = re.sub(r'\W+', ' ', string).lower() 110 | self.string = self.repairString(self.string) 111 | self.words = self.string.split(" ") 112 | if self.words[-1] == '': 113 | del self.words[-1] 114 | fin = open("data/negative.json", "r") 115 | negativeWords = json.loads(fin.read()) 116 | fin.close() 117 | fin = open("data/positive.json", "r") 118 | positiveWords = json.loads(fin.read()) 119 | fin.close() 120 | positiveScore = 0 121 | negativeScore = 0 122 | for i in self.words: 123 | testPositiveWord = self.compareWords(i, positiveWords) 124 | if testPositiveWord is not None: 125 | if positiveWords[testPositiveWord] == 'strong': 126 | positiveScore += 1 127 | else: 128 | positiveScore += 0.5 129 | testNegativeWord = self.compareWords(i, negativeWords) 130 | if testNegativeWord is not None: 131 | if negativeWords[testNegativeWord] == 'strong': 132 | negativeScore += 1 133 | else: 134 | negativeScore += 0.5 135 | 136 | ''' 137 | if i in positiveWords: 138 | if positiveWords[i]=='strong': 139 | positiveScore += 1 140 | else: 141 | positiveScore += 0.5 142 | if i in negativeWords: 143 | if negativeWords[i]=='strong': 144 | negativeScore += 1 145 | else: 146 | negativeScore += 0.5 147 | ''' 148 | if positiveScore + negativeScore == 0: 149 | return {'positive': 0, 'negative': 0} 150 | return {'positive': float(positiveScore) / 151 | (positiveScore + 152 | negativeScore), 'negative': float(negativeScore) / 153 | (positiveScore + 154 | negativeScore)} 155 | 156 | def compareWords(self, aString, aDict): 157 | ''' 158 | This method takes a string and a dictionary as parameters and compares the string with 159 | the keys of dictionary using CompStrings method and returns the most suitable word match 160 | if the probability is greater that 0.9 otherwise it returns None 161 | ''' 162 | probabilities = {} 163 | for i in aDict: 164 | probabilities[i] = self.compStrings(aString, i) 165 | maxProbab = 0 166 | for i in probabilities: 167 | if probabilities[i] > maxProbab: 168 | maxProbab = probabilities[i] 169 | correspondingWord = i 170 | probabilities = 0 171 | if maxProbab > 0.9: 172 | return correspondingWord 173 | else: 174 | return None 175 | 176 | def compStrings(self, str1, str2): 177 | ''' 178 | This method takes two strings as parameters and checks if they are same or not 179 | It returns probability of the comparision 180 | ''' 181 | str1 = list(str1) 182 | str2 = list(str2) 183 | total = len(str1) 184 | count = 0 185 | for i in str1: 186 | try: 187 | str2.index(i) 188 | count += 1 189 | except: 190 | pass 191 | try: 192 | para1 = float(count) / float(total) 193 | except: 194 | para1 = 0 195 | count = 0 196 | total = len(str2) 197 | for i in str2: 198 | try: 199 | str1.index(i) 200 | count += 1 201 | except: 202 | pass 203 | try: 204 | para2 = float(count) / float(total) 205 | except: 206 | para2 = 0 207 | return (para1 + para2) / 2 208 | 209 | def repairString(self, string): 210 | data = { 211 | 'm': 'am', 212 | 'u': 'you', 213 | 'yrs': 'years', 214 | 'ur': 'your', 215 | 'urs': 'yours', 216 | 'tc': 'take care', 217 | 'gn': 'good night', 218 | 'nite': 'night', 219 | 'wat': 'what', 220 | 'abt': 'about', 221 | 'k': 'okay', 222 | 'kk': 'okay', 223 | 'ok': 'okay', 224 | 'don\'t': 'do not', 225 | 'won\'t': 'will not', 226 | 'gonna': 'going to', 227 | 'juz': 'just', 228 | 'jus': 'just', 229 | 'fk': 'fuck', 230 | 'wtf': 'what the fuck', 231 | 'shud': 'should', 232 | 'coz': 'because', 233 | 'cos': 'because'} 234 | string = string.split(" ") 235 | for i in string: 236 | if i in data: 237 | string[string.index(i)] = data[i] 238 | return " ".join(string) 239 | -------------------------------------------------------------------------------- /src/hashtags.py: -------------------------------------------------------------------------------- 1 | from datalink import DatabaseConnectionDown 2 | 3 | 4 | class hashtags: 5 | 6 | ''' 7 | This method extracts all the hashtags present in the tweet 8 | ''' 9 | 10 | def extractHashtagTweet(self): 11 | self.hashtagList = [] 12 | for i in self.tempTweet: 13 | try: 14 | if i[0] == '#': 15 | self.hashtagList.append(i) 16 | except: 17 | pass 18 | ''' 19 | This method extracts all the hashtags by calling extractHashtagTweet method and analyzes 20 | them by searching for them in a database of hashtags 21 | This method takes a string as parameter and returns a dictionary of the probabilities 22 | of the sentiment about that string 23 | DictKeys: 'positive', 'negative' 24 | ''' 25 | 26 | def analyseHashtagTweet(self, tweet): 27 | self.tempTweet = tweet.split(" ") 28 | self.extractHashtagTweet() 29 | if len(self.hashtagList) == 0: 30 | return {"positive": 0, "negative": 0} 31 | 32 | fin = open("data/unigrams-pmilexicon.txt", "r") 33 | resultDict = {} 34 | positiveNum = 0.0 35 | negativeNum = 0.0 36 | positiveProb = [] 37 | negativeProb = [] 38 | tempFile = fin.readlines() 39 | for i in self.hashtagList: 40 | for j in tempFile: 41 | tempLine = j[:-1].split("\t") 42 | if i != tempLine[0]: 43 | continue 44 | else: 45 | positiveNum = float(tempLine[2]) 46 | negativeNum = float(tempLine[3]) 47 | positiveProb.append( 48 | positiveNum / (positiveNum + negativeNum)) 49 | negativeProb.append( 50 | negativeNum / (positiveNum + negativeNum)) 51 | positiveProb = sum(positiveProb) / len(self.hashtagList) 52 | negativeProb = sum(negativeProb) / len(self.hashtagList) 53 | if positiveProb + negativeProb == 0: 54 | return {"positive": 0, "negative": 0} 55 | resultDict["positive"] = positiveProb 56 | resultDict["negative"] = negativeProb 57 | return resultDict 58 | -------------------------------------------------------------------------------- /src/sentiment_plot.py: -------------------------------------------------------------------------------- 1 | import numpy 2 | from matplotlib import pyplot as plt, rc 3 | 4 | 5 | class Plotter: 6 | # def __init__(self,x_labels,y_labels): 7 | # self.x_labels = x_labels 8 | # self.y_labels = y_labels 9 | 10 | def plot(self, x, y, x_labels, y_labels): 11 | fig = plt.figure(figsize=(10, 8), dpi=80) 12 | graph = fig.add_subplot(111) 13 | graph.plot(x, y) 14 | plt.show() 15 | font = {'family': 'Droid Sans', 'size': 8} 16 | rc('font', **font) 17 | graph.set_yticklabels(x_labels) 18 | graph.set_xticklabels(y_labels) 19 | --------------------------------------------------------------------------------