public EmoticonAdapter(Context context, int index, int numColumns) {
switch (index) {
case EMOTICON_WEIBO_INDEX:
mEmoticonBitmap =
Emoticon.getInstance().getSortedEmoticonBitmap().get(Emoticon.EMOTICON_TYPE_WEIBO);
break;
case EMOTICON_LXH_INDEX:
mEmoticonBitmap =
Emoticon.getInstance().getSortedEmoticonBitmap().get(Emoticon.EMOTICON_TYPE_LXH);
break;
case EMOTICON_EMOJI_INDEX:
mEmoticonBitmap =
Emoticon.getInstance().getSortedEmoticonBitmap().get(Emoticon.EMOTICON_TYPE_EMOJI);
break;
default:
throw new IllegalArgumentException("emoticon position is invalid");
}
mContext = context;
mIndex = index;
mNumColumns = numColumns;
mKey = new ArrayList<>(mEmoticonBitmap.keySet());
mTotalSize = mKey.size();
mIconSize = mContext.getResources().getDisplayMetrics().widthPixels / mNumColumns;
}
@Override
public void run() {
// TODO Auto-generated method stub
// POS tagging
List<TaggedToken> taggedTokens = tagger.tokenizeAndTag(tweet);
// identify negation set
HashSet<String> negSet = ng.identifyNegation(tweet);
// a list containing the score of each effective word
ArrayList<Score> scoreList = new ArrayList<Score>();
for (TaggedToken token : taggedTokens) {
if (token.tag.equals("A")) { // adjectives
// System.out.println("token: " + token.token);
String stem = swn.stemmerWords(token.token);
Score wScore = swn.new Score();
wScore = swn.extract(stem);
// if negation of word exist, its pos and neg need to be exchanged
if (negSet.size() > 0 && negSet.contains(token.token)) {
double temp = wScore.pos;
wScore.pos = wScore.neg;
wScore.neg = temp;
}
scoreList.add(wScore);
} else if (token.tag.equals("E")) { // emoticons
int label = en.extractLabel(token.token);
// System.out.println("token: " + token.token);
Score eScore = swn.new Score();
eScore.pos = en.extractPos(label);
eScore.neg = en.extractNeg(label);
eScore.obj = en.extractObj(label);
scoreList.add(eScore);
}
}
// average the score of each effective word of the tweet
tweetScore tScore = new tweetScore();
if (scoreList.size() > 0) {
tScore.averageScore(scoreList);
}
// write the result of each tweet into DB
try {
double obj = 1 - tScore.pos - tScore.neg;
String update =
"update tweets_sentiment_one_month set positivity = "
+ tScore.pos
+ ", negativity = "
+ tScore.neg
+ ", objectivity = "
+ obj
+ " where tweet_id = "
+ this.tweetId;
controller.update(update);
} catch (Exception e) {
// TODO Auto-generated catch block
e.printStackTrace();
}
// if(num % 1000 == 0)
System.out.println(num + " records have been processed!");
}