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TF-IDF + SVM 过滤 约80% 吊子发言
// ==UserScript==
// @name 骟除ShanCu Text Filter (TF-IDF + SVM)
// @namespace https://gitlab.com/6b4tmovet/shancu
// @version 1.0.0
// @description TF-IDF + SVM 过滤 约80% 吊子发言
// @match *://*/*
// @grant GM_getValue
// @grant GM_setValue
// @license MIT
// ==/UserScript==
(function () {
"use strict";
const Storage = {
get(key, defVal) {
try {
if (typeof GM_getValue === "function") {
return GM_getValue(key, defVal);
}
} catch (_) {}
const v = localStorage.getItem("mltf_" + key);
return v !== null ? JSON.parse(v) : defVal;
},
set(key, val) {
try {
if (typeof GM_setValue === "function") {
GM_setValue(key, val);
return;
}
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localStorage.setItem("mltf_" + key, JSON.stringify(val));
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const TFIDF_MODEL = {"vocabulary": {" ": 0, " 2": 1, " 5": 2, " p": 3, " 一": 4, " 上": 5, " 下": 6, " 不": 7, " 之": 8, " 也": 9, " 买": 10, " 今": 11, " 他": 12, " 以": 13, " 会": 14, " 但": 15, " 你": 16, " 像": 17, " 其": 18, " 包": 19, " 原": 20, " 去": 21, " 又": 22, " 反": 23, " 发": 24, " 只": 25, " 可": 26, " 哈": 27, " 哪": 28, " 因": 29, " 图": 30, " 在": 31, " 大": 32, " 太": 33, " 奥": 34, " 女": 35, " 她": 36, " 好": 37, " 如": 38, " 姐": 39, " 学": 40, " 宝": 41, " 小": 42, " 就": 43, " 已": 44, " 并": 45, " 应": 46, " 当": 47, " 很": 48, " 怎": 49, " 想": 50, " 感": 51, " 我": 52, " 或": 53, " 所": 54, " 找": 55, " 是": 56, " 最": 57, " 有": 58, " 根": 59, " 每": 60, " 求": 61, " 没": 62, " 然": 63, " 现": 64, " 用": 65, " 的": 66, " 看": 67, " 真": 68, " 笑": 69, " 第": 70, " 结": 71, " 而": 72, " 能": 73, " 自": 74, " 要": 75, " 让": 76, " 说": 77, " 谢": 78, " 超": 79, " 还": 80, " 这": 81, " 那": 82, " 都": 83, " 长": 84, " 香": 85, "*": 86, ".": 87, "0": 88, "0 ": 89, "15": 90, "2 ": 91, "3 ": 92, "4": 93, "4 ": 94, "50": 95, "6": 96, "66": 97, "8": 98, "?": 99, "at": 100, "ds": 101, "e": 102, "g": 103, "go": 104, "i": 105, "in": 106, "is": 107, "mv": 108, "nb": 109, "oa": 110, "p": 111, "p1": 112, "p2": 113, "p3": 114, "ti": 115, "vp": 116, "yd": 117, "yy": 118, "“": 119, "”": 120, "…": 121, "……": 122, "、": 123, "。": 124, "。。": 125, "。我": 126, "一": 127, "一下": 128, "一人": 129, "一分": 130, "一套": 131, "一家": 132, "一张": 133, "一星": 134, "一样": 135, "一点": 136, "一直": 137, "一真": 138, "上": 139, "上将": 140, "上班": 141, "上衣": 142, "下": 143, "下 ": 144, "下次": 145, "不会": 146, "不听": 147, "不如": 148, "专": 149, "专业": 150, "且": 151, "业": 152, "东北": 153, "两": 154, "个": 155, "个 ": 156, "个小": 157, "个月": 158, "中": 159, "中分": 160, "中国": 161, "中神": 162, "么": 163, "么 ": 164, "么买": 165, "么呀": 166, "么样": 167, "之前": 168, "之后": 169, "乐": 170, "九月": 171, "也": 172, "也太": 173, "也好": 174, "也很": 175, "也想": 176, "也是": 177, "习": 178, "书": 179, "买": 180, "买 ": 181, "买了": 182, "买到": 183, "买的": 184, "乳": 185, "了": 186, "了 ": 187, "了。": 188, "了一": 189, "了哈": 190, "了这": 191, "了，": 192, "了？": 193, "事 ": 194, "五": 195, "五分": 196, "五星": 197, "些 ": 198, "享": 199, "享 ": 200, "亮": 201, "亮 ": 202, "人": 203, "人。": 204, "人，": 205, "什": 206, "什么": 207, "他": 208, "他家": 209, "以": 210, "以 ": 211, "以为": 212, "以用": 213, "以看": 214, "以问": 215, "们 ": 216, "件": 217, "价格": 218, "会": 219, "会不": 220, "伟": 221, "但": 222, "但是": 223, "住": 224, "佑": 225, "佑我": 226, "作": 227, "作者": 228, "你老": 229, "佬": 230, "便": 231, "便宜": 232, "保佑": 233, "俩": 234, "修": 235, "個": 236, "候": 237, "假": 238, "假的": 239, "做": 240, "做饭": 241, "像": 242, "兄": 243, "兄弟": 244, "公": 245, "六星": 246, "其实": 247, "养": 248, "写": 249, "军": 250, "冠": 251, "冠军": 252, "减": 253, "减肥": 254, "几": 255, "几天": 256, "出": 257, "分": 258, "分 ": 259, "分享": 260, "刚": 261, "利给": 262, "到": 263, "到 ": 264, "到了": 265, "到这": 266, "刷": 267, "刷到": 268, "券": 269, "前": 270, "剑": 271, "办": 272, "办 ": 273, "加油": 274, "包": 275, "包包": 276, "化妆": 277, "北": 278, "北人": 279, "医": 280, "午": 281, "卖": 282, "南": 283, "博主": 284, "卧": 285, "卧槽": 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-3.244, -0.847, 0.068, 2.145, 0.6, 2.405, 0.314, -0.722, 1.026, 1.522, 0.144, 0.254, 1.001, 2.786, 0.64, 0.849, 0.739, 0.001, -0.027, 2.033, 0.064, 0.834, -0.015, 0.903, 0.651, 0.042, 1.037, 0.641, 0.912, 0.701, 1.325, -0.81, -0.588, 0.765, 0.734, -0.534, -1.696, 1.612, -0.257, 0.983, 0.759, 1.695, 1.782, 0.081, -0.025, -0.692, 0.127, 0.466, 2.844, 1.287, -0.139, 0.599, 0.36, -0.215, -2.21, 0.955, 1.11, 1.167, -0.976, 0.156, 0.195, -0.006, 0.499, 0.99, -0.224, 0.944, 2.023, 0.417, 0.041, 0.772, 2.76, 2.411, 0.582, -1.058, 0.907, -1.511, 0.258, 2.11, 0.269, 1.668, 0.209, -0.105, 1.263, 1.048, -0.375, -1.039, 1.27, 0.789, 2.33, -2.148, 1.119, -0.153, 0.192, -0.461, -0.018, 1.647, -0.405, -0.852, -0.333, -0.684, 0.436, -1.325, 0.115, 0.127, 0.85, 0.968, -0.227, 0.316, 1.093, 0.36, -0.915, 0.186, 0.577, 2.066, 1.225, -0.063, 0.297, 0.639, 1.201, 1.347, 1.05, -0.104, 1.5, 0.991, 1.145, 2.073, -0.172, 2.518, 0.571, 0.375, -0.193, -2.32, 0.501, -3.167, 2.457, -0.667, -0.372, -1.845, 0.788, -0.885, -0.765, 0.867, 1.014, 1.58, 2.554, -0.423, 1.132, -0.287, 1.544, 1.567, 0.243, 2.67, 0.623, 1.047, 0.952, 0.826, 0.731, 1.174, 0.997, 0.78, 1.036, 1.524, -1.368, -0.44, 0.299, 0.912, 0.348, 0.753, 0.75, 2.66, -0.076, 0.021, 1.184, 1.016, 0.027, -0.037, 0.089, 0.725, 0.731, -0.054, -1.171, 0.109, 1.442, 1.985, 0.825, 0.48, -0.989, -0.974, 0.031, -1.226, 0.904, -1.493, -2.719, 1.76, 1.056, 1.55, 1.656, 0.427, -0.351, -2.561, -1.846, -0.002, 0.358, 0.448, 0.92, 2.541, 1.547, -1.646, 0.678, 0.89, -0.042, 0.977, -0.104, 0.735, 1.326, 1.547, -0.223, 2.289, 0.849, -1.38, 0.409, -0.688, -2.405, 0.75, -0.745, -0.542, 0.999, 0.894, 0.666, 0.58, 1.286, 0.394, 0.687, 0.776, 0.543, -0.17, 0.902, -1.673, 0.086, 2.071, 0.137, 0.73, 1.164, -0.178, 0.309, 0.573, 0.879, 0.864, -1.033, 0.558, 0.498, -0.031, -0.42, 2.415, 0.323, -2.619, 0.427, 0.335, 0.804, -0.001, 0.262, -0.252, 1.271, -0.344, -1.026, -0.327, 2.031, 0.448, 0.923, 1.479, -0.423, 1.216, 0.581, 1.061, 0.243, 1.367, -0.011, -0.044, 3.203, 0.729, 0.226, -0.006, 1.598, -0.776, 0.333, 1.674, 0.983, 0.417, 0.924, 0.285, 0.366, -0.374, -0.467, -1.176, -2.628, 1.383, 0.177, 0.802, 0.44, 0.428, 1.226, 0.564, 0.879, 1.543, 0.956, -0.524, -0.82, -0.227, 0.966, -0.433, -0.858, -2.655, 0.334, 1.803, 0.783, 1.469, 0.413, 2.196, 0.67, 1.679, 2.398, 0.623, -0.271, 1.351, 2.239, 1.182, 1.732, 1.229, -0.276, -0.948, 0.609, 1.787, 2.131, 0.331, 0.005, -1.882, 1.078, 0.443, 1.369, -1.602, 1.122, -6.229, 0.403, 0.897, -0.405, 0.047, 0.103, -0.863, -1.938, -2.299, -0.297, 0.324, 0.271, 1.342, 1.545, 0.893, 2.083, 0.019, 0.061, -0.204, 0.406, -0.55, 0.718, 1.755, 0.011, 0.48, 0.612, 0.12, 0.681, 2.57, 0.666, 1.655, 0.88, 1.686, 0.559, 1.296, 0.347, -0.408, 0.542, 0.247, 0.986, -1.777, 0.26, 0.011, 0.038, 0.649, 0.584, 0.522, 0.833, 0.54, -0.11, 1.183, -0.213, 0.395, 0.818, -0.741, -0.769, 0.136, -1.435, 2.224, 0.175, 0.411, 1.195, 0.187, 1.079, -0.529, 1.16, 1.537, 2.185, 0.674, 0.737, -0.845, -0.154, 2.575, 0.108, 0.536, -1.002, -0.03, -0.447, 0.135, 1.764, 0.874, 1.032, 0.646, 0.851, 0.999, -0.276, -1.384, 0.602, 0.227, 0.785, 0.053, 0.241, 1.005, 0.283, 0.027, -0.013, 0.022, 1.281, 0.479, 0.768, -0.223, -0.747, 0.293, 0.401, -0.381, -1.103, 0.555, 0.447, -0.598, 0.244, 2.488, 1.17, 0.292, 1.026, 1.17, -0.559, 0.049, 0.713, 0.422, 1.244, 1.175, 0.978, -2.041, 0.654, -0.318, -1.047, 0.28, 0.375, -0.159, 0.021, 0.301, 0.008, 1.122, 0.857, -0.335, 0.195, 1.67, 0.435, 1.506, 1.451, 0.523, 0.814, -0.399, 0.898, 1.167, 1.255, 0.173, -0.552, -1.712, 0.288, 0.488, -0.356, -1.213, -0.949, 2.789, 0.319, -0.037, 1.703, 0.729, 2.033, 1.557, 0.578, 2.241, 0.447, 0.414, 0.429, 1.489, 0.88, 0.836, 1.08, 0.679, 0.355, 0.991, -1.704, -0.398, -0.124, -0.377, -1.815, -0.268, 0.537, -0.731, 2.416, 1.654, -1.841, 0.548, -0.193, 0.124, -0.146, 0.926, 1.214, 0.361, 0.193, -2.686, -0.202, 1.256, 3.324, -3.701, 0.899, 0.836, 0.256, -0.694, 0.434, 0.929, 1.171, 0.125, 0.873, -0.554, -1.103, 0.912, 0.671, -2.905, 0.369, 0.64, 1.179, -1.86, 2.699, 1.9, 2.023, 0.671, 0.751, 0.661, 0.689, 0.779, 1.396, 0.866, 0.289, 0.065, 0.561, -0.674, -0.85, 0.495, 0.46, 0.415, 2.461, 0.385, 1.845, 0.259, -0.535, -2.643, 0.83, -0.734, 0.818, -0.101, 1.443, 1.76, 1.336, -0.387, 0.206, 1.959, 1.325, 0.241, -0.492, 3.209, 1.663, -0.054, 0.173, 0.728, 0.568, 0.163, 0.937, 0.391, 1.747, 1.837, 1.239, 3.662, 2.443, -0.394, 1.793, -0.133, -0.598, -0.244, -0.473, -0.645, -1.002, -1.487, 1.22, -0.595, -0.044, -1.518, -1.318, -0.152, 2.236, -3.425, 1.933, -1.381, -1.423, -3.688, -1.483, -1.759, 0.886, -0.99, -0.332, -1.058, -1.59, -11.18, 1.147, 0.157, -0.158, -0.32, -1.614, -2.207], "intercept": -0.626};
function findTextContainers() {
const walker = document.createTreeWalker(
document.body,
NodeFilter.SHOW_TEXT,
null
);
const set = new Set();
let node;
while ((node = walker.nextNode())) {
if (node.nodeValue && node.nodeValue.trim().length > 0) {
set.add(node.parentNode);
}
}
return Array.from(set);
}
/* model */
function charNgrams(text, minN, maxN) {
const clean = text.replace(/\s+/g, "");
const grams = [];
for (let n = minN; n <= maxN; n++) {
for (let i = 0; i <= clean.length - n; i++) {
grams.push(clean.slice(i, i + n));
}
}
return grams;
}
function tfidfVector(text, model) {
const grams = charNgrams(
text,
model.ngram_range[0],
model.ngram_range[1]
);
const tf = {};
for (const g of grams) tf[g] = (tf[g] || 0) + 1;
const vec = new Float32Array(model.idf.length);
for (const t in tf) {
const i = model.vocabulary[t];
if (i !== undefined) {
vec[i] = (1 + Math.log(tf[t])) * model.idf[i];
}
}
return vec;
}
function svmScore(vec, svm) {
let score = svm.intercept;
for (let i = 0; i < vec.length; i++) {
score += vec[i] * svm.coef[i];
}
return score;
}
let panelVisible = Storage.get("panelVisible", true);
function registerMenu() {
if (typeof GM_registerMenuCommand === "function") {
GM_registerMenuCommand(
panelVisible ? "隐藏文本过滤面板" : "显示文本过滤面板",
() => {
panelVisible = !panelVisible;
Storage.set("panelVisible", panelVisible);
updatePanelVisibility();
location.reload();
}
);
}
}
let panel;
function createPanel() {
panel = document.createElement("div");
panel.id = "mltf-panel";
panel.dataset.mlExclude = "1";
Object.assign(panel.style, {
position: "fixed",
top: "80px",
right: "20px",
width: "220px",
padding: "10px",
background: "#fff",
zIndex: 999999,
boxShadow: "0 0 10px rgba(0,0,0,.3)",
fontSize: "13px",
display: panelVisible ? "block" : "none",
border: "1px solid #ccc",
});
// 标题
const title = document.createElement("div");
title.textContent = "文本过滤(调整后刷新网页生效)";
title.style.fontWeight = "bold";
panel.appendChild(title);
// 阈值
const thRow = document.createElement("div");
thRow.textContent = "阈值：";
const thVal = document.createElement("span");
thVal.id = "ml-val";
thRow.appendChild(thVal);
panel.appendChild(thRow);
const slider = document.createElement("input");
slider.type = "range";
slider.min = "-1";
slider.max = "1";
slider.step = "0.01";
slider.id = "ml-th";
panel.appendChild(slider);
// 屏蔽词
panel.appendChild(makeLabel("屏蔽词"));
const blockTA = makeTextarea("ml-block", 3);
panel.appendChild(blockTA);
// 保留词
panel.appendChild(makeLabel("保留词"));
const keepTA = makeTextarea("ml-keep", 3);
panel.appendChild(keepTA);
document.body.appendChild(panel);
bindPanelEvents(slider, thVal, blockTA, keepTA);
}
function makeLabel(text) {
const d = document.createElement("div");
d.textContent = text;
return d;
}
function makeTextarea(id, rows) {
const ta = document.createElement("textarea");
ta.id = id;
ta.rows = rows;
ta.style.width = "100%";
return ta;
}
function bindPanelEvents(slider, thVal, blockTA, keepTA) {
// 初始化
slider.value = Storage.get("scoreThreshold", 0);
thVal.textContent = slider.value;
blockTA.value = Storage.get("blockWords", "");
keepTA.value = Storage.get("keepWords", "");
slider.addEventListener("input", () => {
thVal.textContent = slider.value;
Storage.set("scoreThreshold", Number(slider.value));
});
blockTA.addEventListener("change", () => {
Storage.set("blockWords", blockTA.value);
});
keepTA.addEventListener("change", () => {
Storage.set("keepWords", keepTA.value);
});
}
function updatePanelVisibility() {
if (panel) {
panel.style.display = panelVisible ? "block" : "none";
}
}
function isExcludedNode(node) {
let el = node;
while (el && el !== document.body) {
if (el.dataset && el.dataset.mlExclude === "1") {
return true;
}
el = el.parentNode;
}
return false;
}
function applyFilter() {
const threshold = Storage.get("scoreThreshold", 0);
const blockList = Storage.get("blockWords", "")
.split("\n").map(w => w.trim()).filter(Boolean);
const keepList = Storage.get("keepWords", "")
.split("\n").map(w => w.trim()).filter(Boolean);
const TAG_BLACKLIST = new Set([
"INPUT",
"TEXTAREA",
"SELECT",
"BUTTON"
]);
for (const node of findTextContainers()) {
if (isExcludedNode(node)) continue;
if (!node.innerText || node.dataset.mlDone) continue;
if (TAG_BLACKLIST.has(node.tagName)) continue;
const text = node.innerText.trim();
if (text.length < 3) continue;
if (blockList.some(w => text.includes(w))) {
node.textContent = "〇〇〇屏蔽词屏蔽";
node.dataset.mlDone = "1";
continue;
}
if (keepList.some(w => text.includes(w))) {
node.dataset.mlDone = "1";
continue;
}
const vec = tfidfVector(text.replace(/[^\u4e00-\u9fa5a-zA-Z0-9]/g, ' '), TFIDF_MODEL);
let score = svmScore(vec, SVM_MODEL);
let finalScore = text.length > 0 ? (score / text.length) : 0;
if (finalScore < -1) finalScore = -1;
if (finalScore < threshold) {
const displayScore = finalScore.toFixed(2);
node.textContent = `〇〇〇score=${displayScore}`;
}
node.dataset.mlDone = "1";
}
}
new MutationObserver(applyFilter)
.observe(document.body, { childList: true, subtree: true });
registerMenu();
createPanel();
applyFilter();
})();