Score the sentiment of a review or message with a bilingual positive/negative lexicon and negation handling.
用中英文正负情感词典对评论、留言打分,正确处理"不""没"等否定词,列出命中的正负词。
A lexicon-based sentiment analyser that works on both Chinese and English text. It scans the tokenised text against positive and negative word lists, counts how many of each it hit, and subtracts a penalty for negations such as "not", "no", "不" and "没" so that phrases like "not good" are not scored as praise. The output is a score, a label, and the exact words that drove the decision — which makes it easy to audit and tune for your own domain.
一个基于情感词典的分析器,中英文都能处理。它把文本分词后逐词匹配正负情感词表,分别统计命中数量,并对 "not"、"no"、"不"、"没" 等否定词施加惩罚,所以 "not good" 和 "不好" 不会被当成表扬。输出包含得分、判定标签,以及具体命中的词——便于审计,也便于针对你的业务词表自行扩展。
No, and that is deliberate. A lexicon is transparent, instant, free and works offline, while a trained model needs a download and a service. For triage and teaching, the lexicon often tells you more because you can see every word it used.
不一样,而且是有意为之。词典法透明、即时、免费、可离线;训练好的模型需要下载和服务。做初筛与教学时,词典反而信息量更大——因为你能看到它依据的每一个词。
A negation word immediately before a sentiment word applies a 0.5 penalty, so "not good" and "不好" both pull the score down instead of being counted as praise.
情感词前紧邻的否定词会扣 0.5 分,"not good" 和 "不好" 都会拉低总分,而不是被当作赞扬。
Lexicon methods miss them — a sarcastic "great job" still scores positive. The matched-words list is the fastest way to spot that case and extend the word list for your own domain.
词典法会漏掉它们——反讽的"干得漂亮"仍然是正分。查看命中词列表是发现这类情况、并按你的领域扩充词表最快的方式。
No. The scoring is pure JavaScript in your browser.
不会。打分是浏览器内的纯 JavaScript。
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