A Fuzzy Computing Model for Identifying Polarity of Chinese Sentiment Words

<div>Performance of MBOM and FCMWFP in different parameter <svg height="9.49473pt" id="M109" style="vertical-align:-0.2063999pt" version="1.1" viewbox="-0.0498162 -9.28833 6.74143 9.49473" width="6.74143pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"><g transform="matrix(.013,0,0,-0.013,0,0)"><path d="M485 418C485 434 469 451 441 451C389 451 312 387 255 335C221 304 194 278 159 242H157L259 678C264 698 264 710 255 710C242 710 183 681 96 673L90 643L128 642C165 641 171 640 162 602L24 -5L31 -12C51 -4 81 4 110 9L145 181C154 195 180 220 205 242C233 170 263 106 291 53C318 1 335 -12 360 -12C383 -12 425 0 483 82L463 105C437 79 411 57 400 57C388 57 378 68 355 110C329 156 288 244 268 298C299 333 355 376 405 376C426 376 440 373 448 368C453 365 465 368 468 373C477 386 485 405 485 418Z" id="g50-108"></path><glyph.data ascent="3443" descent="-2856" horiz-adv-x="509" vert-adv-y="509"></glyph.data></g></svg> in KSL.</div>

Computational Intelligence and Neuroscience

fig2

Figure 2

Figure 2: A Fuzzy Computing Model for Identifying Polarity of Chinese Sentiment Words 